An Interval Tree Approach to Predict Forest Fires using Meteorological Data

Fires in nature are caused by moisture content in the burning material, which is dependent on the values of the climatic elements. The occurrence of these fires in Serbia is becoming more common, depending on the intensity and duration have a major impact on the state of vegetation. The aim of this study was to determine the association between changes in air temperature and the dynamics of the appearance of forest fires. To study the association of these properties were used Pearson correlation coefficients. The analysis is based on meteorological data obtained from meteorological station in Negotin for the period 1991-2010. Research has found that the annual number of fires, correlating with an average annual air temperature (p = 0.317, ? = 0.21). Also, it was found that the annual number of fires positive, medium intensity, correlate with the absolute maximum air temperature (p = 0.578, ? = 0.26), but not statistically significant (p> 0.05).

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Fire-growth modelling using meteorological data with random and systematic perturbations

International Journal of Wildland Fire ◽

10.1071/wf06069 ◽

2007 ◽

Vol 16 (2) ◽

pp. 174 ◽

Cited By ~ 20

Author(s):

Kerry Anderson ◽

Gerhard Reuter ◽

Mike D. Flannigan

Keyword(s):

Wind Speed ◽

Growth Model ◽

Forest Fires ◽

Meteorological Data ◽

Random Perturbation ◽

Weather Forecast ◽

Fire Spread ◽

Fire Growth ◽

Hourly Data ◽

Systematic Perturbation

The focus of this investigation is to quantify the effects of perturbations in the meteorological data used in a fire-growth model. Observed variations of temperature, humidity, wind speed, and wind direction are applied as perturbations to hourly values within a simulated weather forecast to produce several forecasts. In turn, these are used by a deterministic eight-point fire-growth model to produce an ensemble of possible final fire perimeters. Two studies were conducted to assess the value of applying perturbations. In the first study, fire growth using detailed, one-minute data was compared to growth based on the more commonly used hourly data. Results showed that the detailed weather produced fire growth larger and wider than the hourly based data. By applying perturbations, variations in the flank and back-fire spread were captured by the random-perturbation model while the forward spread fell within the 20 to 30% probability prediction. A sensitivity analysis based on the observed variations showed that wind speed accounted for a 44% difference in area burned, while temperature accounted for only a 16% difference. In the second study, case studies were conducted on four observed forest fires in Wood Buffalo National Park. Results showed that daily fire-growth predictions using simulated weather forecasts over-predicted fire growth using actual hourly weather observations by 27%. Systematic-perturbation models best compensated for this with most fire growth falling within the predicted range of the models (52 out of 63 days).

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Modelling dry thunderstorm environment during a wildfire episode in Portugal

10.5194/egusphere-egu2020-10796 ◽

2020 ◽

Author(s):

Flavio T. Couto ◽

Maksim Iakunin ◽

Rui Salgado ◽

Paulo Pinto ◽

Tânia Viegas ◽

...

Keyword(s):

Forest Fires ◽

Meteorological Data ◽

Grid Point ◽

Horizontal Resolution ◽

Atmospheric Environment ◽

Atmospheric Conditions ◽

Burned Areas ◽

Realistic Representation ◽

High Base ◽

Fire Ignition

Under future climate uncertainties, a better understanding of wildfires is necessary both from physical and operational points of view, which are the goals of the CILIFO (Centro Ib&#233;rico para la Investigacion y Lucha contra Incendios Forestales) Interreg POCTEP project. Among several sources of fire ignition, lightnings are the main natural source of wildfires and an important contributor to burned areas in many regions. In 2017, devastating forest fires were reported in Portugal. The fires near Pedr&#243;g&#227;o Grande created a huge wall of flames, killing at least 60 people. The goal of this study is to discuss the atmospheric conditions that were supportive of lightning flashes to cause a fire during this event, as well as to check the possibility to correctly diagnose cloud-to-ground flashes using high resolution simulations with the non-hydrostatic atmospheric Meso-NH model. A set of meteorological data was used to validate the model results and to describe the prevailing atmospheric environment during the afternoon of 17th June 2017 over central Portugal. The Portuguese Institute for Sea and Atmosphere (IPMA) provided the data for this study. The Meso-NH model was configured in order to provide an explicit representation of the clouds and their electrical activity, through the activation of the CELLS electrical scheme. The ICE3 microphysical scheme predicts the mixing ratio of six atmospheric water categories. The Meso-NH system also includes a grid point radar diagnostic given by the total equivalent radar reflectivity, as well as a Plan Position Indicator (PPI) that is a representation mode in which sweeping cones are projected on a horizontal plane determined by scanning the atmosphere at constant elevation. The description of the electrical state of a thunderstorm is based on the monitoring of the electrical charge densities, the computation of the electric field and the production of lightning flashes. The cloud charging involves mostly the non-inductive mechanism, and both Intra-Cloud (IC) and Cloud-to-Ground (CG) flashes are considered. The CELLS scheme provides a realistic representation of the electrical properties of precipitating cloud systems. The simulation was carried out with two nested domains of 4 km and 1 km horizontal resolution. Concerning the atmospheric conditions, the dry thunderstorm environment configured a perfect scenario for the natural ignition and evolution of some fires, since lightning activity came from high-base thunderstorms with relatively dry air at lower levels favouring the evaporation of rain before it reaches the ground, as well as intense outflows. Therefore, the fires on 17th June 2017 occurred in an exceptional hot day, with fire ignitions in places with complex terrain and a favourable vegetation state producing uncontrolled wildfires. The spatial distribution of the simulated CG lightnings showed a good agreement with the lightning strokes obtained from the national lightning detection network. Besides the identification of favourable conditions for the occurrence of wildfires, this study introduces a possible application of the Meso-NH electrical scheme, namely the study of forest fire ignition by lightning strokes.

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Radionuclide atmospheric transport after the forest fires in the Chernobyl Exclusion zone in 2015-2018: An impact of the source term parameterization and input meteorological data on modeling results

10.5194/egusphere-egu2020-10066 ◽

2020 ◽

Author(s):

Mykola Talerko ◽

Ivan Kovalets ◽

Shigekazu Hirao ◽

Mark Zheleznyak ◽

Yuriy Kyrylenko ◽

...

Keyword(s):

Real Time ◽

Forest Fires ◽

Source Term ◽

Atmospheric Transport ◽

Meteorological Data ◽

Atmospheric Dispersion ◽

Lagrangian Model ◽

Wildland Fires ◽

Exclusion Zone ◽

The Impact

The highly contaminated Chernobyl exclusion zone (ChEZ) still remains a potential source of the additional atmosphere radioactive contamination due to forest fires there. The possible radionuclide transport outside the ChEZ in the direction of populated regions (including Kyiv, 115 km from the ChEZ borders) and its consequences for people health is a topic of a constant public concern in Ukraine and neighboring countries. The problem of additional radiation exposure of fire-fighters and other personnel within the ChEZ during forest fires is actual too. The reliable models of radionuclide rising and following atmospheric transport, which should be integrated with data of stationary and mobile radiological monitoring, are necessary for real-time forecast and assessment of consequences of wildland fires.Results of intercomparison of models developed within the set of the national and international projects are presented, including: i) the point source term model of Atmospheric Dispersion Module (ADM) of the real -time online decision support system for offsite nuclear emergency &#8211; RODOS, which development was funded by EU; ii) the specialized new tool for modeling radionuclide dispersion from the polygons of the fired areas using the Lagrangian model LASAT incorporated into RODOS system; iii) the Lagrangian-Eulerian atmospheric dispersion model LEDI using a volume source term and including a module for calculation of&#160; parameters of a convective plume&#160; formed over a fire area; iv) the Lagrangian model of Fukushima University. All atmospheric transport models use the results of the numerical weather forecast model WRF as the input meteorological information.The models evaluation was carried out using the measurement data during large wildland fires occurred in ChEZ in 2015 and June 2018, including the 137Cs and 90Sr volume activity measured with the monitoring network within the Zone and results due to special measurements with a mobile radiological laboratory outside it.The sensitivity of atmospheric transport modeling results was estimated to: 1) internal parameterization of different models, first of all, parameterization of the value of the deposited radionuclide fraction re-entering into the atmosphere during forest fires, 2) different parameterization of the source term formed due to the forest fire; 3) quality of input meteorological information, including the space and time step of the used WRF model grid, and the impact of chosen parameterization of some WRF modules (e.g. the atmospheric boundary layer module) on the atmospheric transport model results. Additionally, results of forest fires consequences modeling was compared which were obtained with different sets of input meteorological data: the WRF forecast of metrological fields (on-line calculations) and the similar WRF calculations on the base of objective analysis results.

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INFLUENCE OF THE METEOROLOGICAL CONDITIONS ON FOREST FIRES OCCURRENCES IN LICHINGA DISTRICT, NORTHERN MOZAMBIQUE

FLORESTA ◽

10.5380/rf.v45i3.33742 ◽

2015 ◽

Vol 45 (3) ◽

pp. 577 ◽

Cited By ~ 2

Author(s):

Aires Afonso Mbanze ◽

Antonio Carlos Batista ◽

Alexandre França Tetto ◽

Henrique Soares Koehler ◽

Jose Bernardo Manteiga

Keyword(s):

Forest Fires ◽

Meteorological Data ◽

Great Influence ◽

Meteorological Conditions ◽

Meteorological Station ◽

Weather Data ◽

Monitoring And Control ◽

Tukey Test ◽

Daily Weather Data ◽

And Control

AbstractThe aim of this study was to assess the influence of meteorological conditions on the fire occurrences in forest stands of Lichinga district, in the period from 2010 to 2012. Data about fire occurrences records of the district of Lichinga and two others close districts (Lago and Sanga) were provided by the Center for Monitoring and Control of Forest Fires (CCMIF) of the company Chikweti. Daily weather data: temperature, rainfall and relative humidity of the same period, recorded at 13:00 PM, by the meteorological station of the Institute of Agronomic Research of Mozambique (IIAM) in Lichinga district were also provided to this work. Meteorological data were submitted to regression analysis and Tukey test. The results showed a significant variation in temperature and humidity on both tests. The overlapping of fire occurrences and meteorological variables, suggested a great influence of the meteorological conditions in the occurrence of fires, mainly due to the very long dry periods. In 2010 there was a delay in the occurrence of fires; this was related to the rainy season which was slightly longer. September and October was the months that recorded the highest number of fire occurrences throughout the studied period.ResumoInfluência das condições meteorológicas na ocorrência dos incêndios florestais no distrito de Lichinga, norte de Moçambique. O objetivo deste estudo foi avaliar a influência das variáveis meteorológicas na ocorrência de incêndios em povoamentos florestais no distrito de Lichinga, no período de 2010 a 2012. Para tal, foram analisados os registros de ocorrências de incêndios do distrito de Lichinga e de outros dois distritos vizinhos (Lago e Sanga), disponibilizados pelo Centro de Controle e Monitoramento de Incêndios Florestais (CCMIF) da empresa Chikweti Forest of Niassa, e dados meteorológicos diários de temperatura (máxima e mínima), precipitação e umidade relativa, do mesmo período, registrados às 13 horas, pela estação meteorológica do Instituto de Investigação Agronômica de Moçambique em Lichinga (IIAM-Lichinga). Os dados meteorológicos foram submetidos ao teste de análise de regressão e ao teste de Tukey, tendo sido observado uma variação significativa da temperatura e umidade em ambos os testes. A sobreposição das ocorrências dos incêndios com as variáveis meteorológicas demostrou uma grande influênca dessas variáveis na ocorrência dos incêndios, principalmente devido aos períodos secos prolongados. No ano 2010 observou-se um atraso na ocorrência dos incêndios, devido ao período chuvoso que foi ligeiramente mais longo. Os meses que registraram maior número de ocorrências em todo o período foram setembro e outubro.Palavras-chave: Povoamentos florestais; variáveis meteorológicas; prevenção de incêndios florestais.

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

10.5194/egusphere-egu21-14602 ◽

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

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 &#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).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.

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Waldbrandmanagement im Kanton Wallis und Lehren aus dem Brand von Visp im Jahr 2011

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2019.0251 ◽

2019 ◽

Vol 170 (5) ◽

pp. 251-257

Author(s):

Philipp Gerold

Keyword(s):

Forest Fires ◽

Fire Management ◽

Meteorological Data ◽

Fire Danger ◽

Fire Prevention ◽

Good Test ◽

Forest Protection ◽

Management Concept ◽

Prevention Activities ◽

The Impact

Fire management in the canton of Valais and lessons learnt from the Visp fire 2011 Long-lasting dry weather conditions without precipitation in the Valais make the area very prone to forest fires. Following the large fire in Leuk in summer 2003, the cantonal authorities developed between 2006 and 2008 a fire management concept mainly focusing on prevention activities and on completing the water points net on the whole territory. A very important outcome of this concept is the definition of priority regions where a detailed fire prevention and control concept should be implemented. Concerning the fire danger rating, the canton of Valais acquired the “Incendi” system from the canton of Grisons, which uses meteorological data from the MeteoSwiss weather stations network to provide a daily calculation of several fire weather indices. In case of high fire danger, a fire ban in the open can be decreed basing on the cantonal fire law. The 110 ha large forest fire in Visp (2011) represented a very good test for the new cantonal fire management concept, especially for what concerns the impact on the forest protection functions. The very dry 2018 summer and the forecasted climatic change will in the future give a very central role to the fire prevention activities.

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The recent fire history of Barron Township, Algonquin Park

Canadian Journal of Botany ◽

10.1139/b77-180 ◽

1977 ◽

Vol 55 (11) ◽

pp. 1524-1538 ◽

Cited By ~ 30

Author(s):

Les C. Cwynar

Keyword(s):

Forest Fires ◽

Fire History ◽

Meteorological Data ◽

Historical Documents ◽

Southern Ontario ◽

Mean Frequency ◽

Severe Fire ◽

Field Evidence ◽

History Of ◽

Time Required

The recent fire history of Barron Township (18 600 ha) in Algonquin Park was studied by examining historical documents and dating past forest fires using dendrochronological techniques. Lightning is still a major ignition source. Over the 36-year period of 1939-1974, lightning accounted for 48.5% of all fires at a rate of 0.19 fires per year per 10 000 ha. Dendrochronological data show that 16 fires burned during the 225-year presuppression interval from1696 to 1920 for a mean frequency of 14.1 years. Five major fires (1875, 1864, 1854, 1780, and 1763) each burned at least half of the township with a mean frequency of 45 years. Meteorological data show that drought prevailed across southern Ontario during 1875, when major fires burned in Barron Township and northern Minnesota, supporting the suggestion that severe fire years correspond with periods of subcontinental drought. Similarly, 1864 appears to have been a major fire year in Barron as it certainly was in Minnesota. The recent fire rotation, i.e., the average time required to burn an area equivalent to the size of the study area, is about 70 years. Field evidence and increment borings suggest that the present vegetation mostly originated as a consequence of the fire of 1875.

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A Neural Network Model for Wildfire Scale Prediction using Meteorological Factors

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35258 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2646-2650

Author(s):

Lavanya I

Keyword(s):

Neural Network ◽

Forest Fire ◽

Forest Fires ◽

Meteorological Data ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Fire Incident ◽

Forested Areas ◽

Long Term Impact

Forest fires are natural hazards defined as movements of fire through unregulated and uncontrolled forested areas. They pose a permanent risk of loss of forest and forest land. The ability to reliably forecast the region that could be involved in a forest fire incident will help to optimize fire prevention efforts. It appears that Portugal may theoretically make better use of the wildfire risk assessment. More than any other region in Europe, it is a country overrun by wildfires. It has a large amount of forest. Forest fires have a long-term impact on the climate because they contribute to deforestation and global warming, which is one of the main causes of the phenomenon. This research employs Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models with meteorological parameters as inputs to anticipate forest fires as a means of safeguarding forest biodiversity. The results indicate that using meteorological data, it is possible to anticipate the severity of a forest fire at the beginning.

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

10.5121/csit.2020.101915 ◽

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.

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