Forest Fire Danger Research Articles

GIS-TECHNOLOGIES AND MATHEMATICAL SIMULATION TO PREDICT LIGHTNING-CAUSED FOREST FIRE DANGER

Context. The components of the geoinformation system for monitoring, forecasting and assessment of forest fire danger caused by thunderstorm activity are developed. Objective of the work is to create an embedded software tool for physically based forecasting, monitoring and evaluation of the probability of forest fire occurrence as a result of the impact of a lightning discharge on a tree trunk. Method. Structural analysis is used to design elements and information flows inside and outside of the developed geographic information system. Mathematical modeling is used to determine the parameters of tree ignition by the cloud-to-ground lightning discharge. Mathematically, the process of tree trunk heating is described using a system of non-stationary heat conduction equations with a source part responsible for the heat release according to the Joule-Lenz law in the core of the tree trunk. The finite difference method is used to solve the differential heat equation. Finite-difference analogues are solved by the double-sweep method. Program realization is implemented in the built-in high-level language. The probability theory (conditional probability) is used to develop a probabilistic criterion for forest fire danger estimation. Results. A software tool is developed to estimate the tree ignition delay time as a result of the impact of a cloud-to-ground lightning discharge. The GIS-system component is developed in the high-level programming language Python. We have obtained probability distribution of forest fire occurrences from thunderstorms for the territory of the Timiryazevsky forestry in the Tomsk region is obtained. Conclusions. We have proposed a physically proved method for forecasting, monitoring and assessing forest fire danger caused by thunderstorm activity. The deterministic mathematical model is used to simulate tree ignition by the cloud-to-ground lightning discharge in conjunction with the probabilistic criterion for assessing forest fire danger. We have analysed forest fire danger for a typical territory of the Tomsk region (Timiryazevskiy forestry).

Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study

Thuan Chau is a serious district affected by forest fire in Vietnam, especially in 2016; however, no forest fire prediction research has been conducted for this region. Thus, knowledge of spatial patterns of fire danger of the district plays a key role in forest succession and ecological implications. This study's aim was to analyze the spatial pattern of fire danger for the tropical forest of Thuan Chau district using advanced machine learning algorithms, Support Vector Machine classifier (SVMC), Random Forests (RF), and Multilayer Perceptron Neural Network (MLP-Net). For this purpose, a GIS database for the study area was established with 564 forest fire locations and ten forest fire variables. Then, Pearson correlation method was used to assess the correlation of the variables with the forest fire. In the next step, three forest fire danger models, SVMC, RF, and MLP-Net, were trained and validated. Finally, global performance of these models was assessed using the classification accuracy (ACC), Kappa statistics (KS), Area under the curve (AUC). In addition, Wilcoxon signed-rank test was employed to check the prediction performance of these models. The result shows the three models performed well; however, the MLP-Net model has the highest prediction performance (ACC = 81.7, KS = 0.633, and AUC = 0.894), followed by the RF model (ACC = 81.1, KS = 0.621, and AUC = 0.883), and the SVMC model (ACC = 80.2, KS = 0.604, and AUC = 0.867). The result in this study is useful for the local authority and forest manager in forest management and fire suppression.

Exploring the future change space for fire weather in southeast Australia

High-resolution projections of climate change impacts on fire weather conditions in southeast Australia out to 2080 are presented. Fire weather is represented by the McArthur Forest Fire Danger Index (FFDI), calculated from an objectively designed regional climate model ensemble. Changes in annual cumulative FFDI vary widely, from − 337 (− 21%) to + 657 (+ 24%) in coastal areas and − 237 (− 12%) to + 1143 (+ 26%) in inland areas. A similar spread is projected in extreme FFDI values. In coastal regions, the number of prescribed burning days is projected to change from − 11 to + 10 in autumn and − 10 to + 3 in spring. Across the ensemble, the most significant increases in fire weather and decreases in prescribed burn windows are projected to take place in spring. Partial bias correction of FFDI leads to similar projections but with a greater spread, particularly in extreme values. The partially bias-corrected FFDI performs similarly to uncorrected FFDI compared to the observed annual cumulative FFDI (ensemble root mean square error spans 540 to 1583 for uncorrected output and 695 to 1398 for corrected) but is generally worse for FFDI values above 50. This emphasizes the need to consider inter-variable relationships when bias-correcting for complex phenomena such as fire weather. There is considerable uncertainty in the future trajectory of fire weather in southeast Australia, including the potential for less prescribed burning days and substantially greater fire danger in spring. Selecting climate models on the basis of multiple criteria can lead to more informative projections and allow an explicit exploration of uncertainty.

ВЗАЄМОЗВ'ЯЗОК І ВЗАЄМОЗУМОВЛЕНІСТЬ ПРОЯВУ СТИХІЙНИХ ЯВИЩ У ГІРСЬКО-ЛІСОВИХ ЕКОСИСТЕМАХ

Розглянуто взаємозв'язки та взаємовпливи між стихійними явищами у гірсько-лісових екосистемах Карпат – паводками, ерозією ґрунту, селями, зсувами, вітровалами, сніголамами, сходженням снігових лавин, періодичними засухами, лісовими пожежами та іншими. Наведено перелік стихійних явищ, які, взаємодіючи між собою, підсилюють окремі з них, уповільнюючи цим самим інші. З'ясовано, що внаслідок антропогенних змін лісових формацій інтенсифікувалися шкідливі процеси, знизилася продуктивність і стійкість лісових насаджень. Проаналізовано причини виникнення окремих видів стихійних явищ, їх вплив на лісові насадження в нинішній час і в перспективний період. Відзначено значну шкідливість для лісового господарства перманентних вітровалів. Наголошено на збитковості для лісових екосистем катастрофічних сніголамів і сніговалів, які деформують деревину, знижують її приріст за висотою і запасом, та є осередками виникнення грибкових захворювань. Зазначено, що у місцях стихійних явищ зростає небезпека лісових пожеж, знижується естетична і рекреаційна цінність лісових ландшафтів. Наведено перелік заходів, що послаблюють дію негативних природних стихійних явищ та підвищують стійкість лісових екосистем до антропогенного фактору. Важливим є твердження, що вітростійкість насаджень залежить від їх породного складу. Найбільш вітростійкими є насадження за участі ялиці білої та листяних порід.

Fire frequency analysis for different climatic stations in Victoria, Australia

Frequent occurrence of fire events will have severe impact on Victoria’s water supply catchments. Hence, it is important to perform fire frequency analysis to obtain fire frequency curves (FFC) on fire intensity using Forest Fire Danger Index (FFDI) at different parts of Victoria. FFDI is a measure of fire initiation, spreading speed and containment difficulty. FFC will guide water harvesting by providing information with regard to future fire events and the subsequent impact on catchment yield. Five probability distributions, namely normal, Log Pearson Type III (LPIII), gamma, log-normal and Weibull distributions were used for the development of FFCs at ten selected meteorological stations spread all over Victoria. LPIII distribution was identified as the best fit distribution for Victoria and subsequently applied for an additional 30 more stations to show spatial variability for the entire Victoria.

広島県における人口密度,植生,気象要因を用いた林野火災発生危険度の予測

広島県における人口密度,植生,気象要因を用いた林野火災発生危険度の予測

A Hybrid Intelligence System Based on Relevance Vector Machines and Imperialist Competitive Optimization for Modelling Forest Fire Danger Using GIS

This article proposes and verifies a novel intelligence approach for modelling forest fire danger, namely ICA-RVM, developed based on Relevance Vector Machine (RVM) and Imperialist Competitive Algorithm (ICA), state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling. RVM is used to establish a prediction model that computes probability of fire danger, whereas ICA is adopted to optimize the prediction model. The tropical forest at Gia Lai province, Central Highland (Vietnam), was used as a case study. Area under the curve (AUC) and statistical measures were used to assess the model performance. The result showed that the proposed model achieves high performances; AUC is 0.842 and 0.793 on the training dataset and the validation dataset, respectively. Compared to two benchmarks, Random Forests and Support Vector Machine, the proposed model performs better. Therefore, the propose ICA-RVM is a valid alternative system for forest fire danger modeling.

Determination of thermophysical and thermokinetic characteristics of forest combustible materials

The danger of forest fires and their large-scale consequences is facing humanity more and more sharply from year to year. We carried out experimental studies to determine the thermal characteristics (thermal conductivity, heat capacity, thermal diffusivity) and thermokinetic characteristics (activation energy, pre-exponential factor) of typical forest combustible materials and their mixtures for a wide range of temperatures (298–423 K). A generalization of experiments for typical forest combustible materials of the Far Eastern Federal District of Russia was performed. The established experimental values can be used for mathematical modeling of occurrence, propagation and extinguishing of forest fires.

Numerical investigation of localization and suppression of thermal decomposition of forest combustible materials using specialized water supply

The danger of forest fires and their large-scale consequences are becoming ever more complex problems for mankind every year. The results of numerical studies of heat transfer processes under suppressing the thermal decomposition reaction of forest combustible materials by films and groups of water droplets are presented. We used forest combustible materials, typical for the Siberian region. We have established the relationship between the dimensions of the heated near-surface layer of the material and the thickness of the film layer of the liquid that evaporated when the thermal decomposition reaction was suppressed in forest combustible materials. Typical times of suppression of the thermal decomposition reaction of typical forest combustible materials are given when the water film on the surface evaporates. The influence of the mutual arrangement of droplets on the surface of the forest combustible material and the temperature of the outer vapor-gas mixture on the times of suppression of its pyrolysis is revealed. The results can be used to develop technologies for extinguishing fires, containment of flame sources and combustion front.

Forest and Land Fire Danger Mapping Based on Land Physical Parameters in Sumatera and Kalimantan Region of Indonesia

Indonesia is a country that vulnerable to land and forest fires danger. This is not out of the presence of peatland in Indonesia is quite wide, which are mostly located in Sumatara and Kalimantan. The nature of peatlands are capable to store water in large quantities, but the surface dries quickly and become flammable during the dry season. This research aims to perform mapping areas that vulnerable to land and forest fires danger based on the physical parameters. Based on the results obtained can be used as the basis for develop land and forest fires early warning system in Indonesia. This research use GIS (Geographic Information System) for processing mapping of physical parameters which consists of Land Use Map, Topography Map and Soil Map in Sumatra and Kalimantan to get the forest and land fires danger map. The results of the mapping areas that vulnerable to forest and land fire based on the physical parameters resulting from this study have uniform patterns with the distribution and density of hotspots in the Sumatra and Kalimantan over the last 10 years (2006-2015). This is reinforced by the result of mapping accuracy test, where the percentage number of hotspots during the last 10 years both in Sumatera and Kalimantan area which is exactly vulnerable up to very vulnerable to land and forest fires danger by 74%.

Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire

AbstractChanging frequencies of extreme weather events and shifting fire seasons call for enhanced capability to forecast where and when forested landscapes switch from a nonflammable (i.e., wet fuel) state to the highly flammable (i.e., dry fuel) state required for catastrophic forest fires. Current forest fire danger indices used in Europe, North America, and Australia rate potential fire behavior by combining numerical indices of fuel moisture content, potential rate of fire spread, and fire intensity. These numerical rating systems lack the physical basis required to reliably quantify forest flammability outside the environments of their development or under novel climate conditions. Here, we argue that exceedance of critical forest flammability thresholds is a prerequisite for major forest fires and therefore early warning systems should be based on a reliable prediction of fuel moisture content plus a regionally calibrated model of how forest fire activity responds to variation in fuel moisture content. We demonstrate the potential of this approach through a case study in Portugal. We use a physically based fuel moisture model with historical weather and fire records to identify critical fuel moisture thresholds for forest fire activity and then show that the catastrophic June 2017 forest fires in central Portugal erupted shortly after fuels in the region dried out to historically unprecedented levels.

Use of regionalisation approach to develop fire frequency curves for Victoria, Australia

It is important to perform fire frequency analysis to obtain fire frequency curves (FFC) based on fire intensity at different parts of Victoria. In this paper fire frequency curves (FFCs) were derived based on forest fire danger index (FFDI). FFDI is a measure related to fire initiation, spreading speed and containment difficulty. The mean temperature (T), relative humidity (RH) and areal extent of open water (LC2) during summer months (Dec–Feb) were identified as the most important parameters for assessing the risk of occurrence of bushfire. Based on these parameters, Andrews’ curve equation was applied to 40 selected meteorological stations to identify homogenous stations to form unique clusters. A methodology using peak FFDI from cluster averaged FFDIs was developed by applying Log Pearson Type III (LPIII) distribution to generate FFCs. A total of nine homogeneous clusters across Victoria were identified, and subsequently their FFC’s were developed in order to estimate the regionalised fire occurrence characteristics.

Climatological Variability of Fire Weather in Australia

AbstractLong-term variations in fire weather conditions are examined throughout Australia from gridded daily data from 1950 to 2016. The McArthur forest fire danger index is used to represent fire weather conditions throughout this 67-yr period, calculated on the basis of a gridded analysis of observations over this time period. This is a complementary approach to previous studies (e.g., those based primarily on model output, reanalysis, or individual station locations), providing a spatially continuous and long-term observations-based dataset to expand on previous research and produce climatological guidance information for planning agencies. Long-term changes in fire weather conditions are apparent in many regions. In particular, there is a clear trend toward more dangerous conditions during spring and summer in southern Australia, including increased frequency and magnitude of extremes, as well as indicating an earlier start to the fire season. Changes in fire weather conditions are attributable at least in part to anthropogenic climate change, including in relation to increasing temperatures. The influence of El Niño–Southern Oscillation (ENSO) on fire weather conditions is found to be broadly consistent with previous studies (indicating more severe fire weather in general for El Niño conditions than for La Niña conditions), but it is demonstrated that this relationship is highly variable (depending on season and region) and that there is considerable potential in almost all regions of Australia for long-range prediction of fire weather (e.g., multiweek and seasonal forecasting). It is intended that improved understanding of the climatological variability of fire weather conditions will help lead to better preparedness for risks associated with dangerous wildfires in Australia.

Assessing the risk to the conservation status of temperate rainforest from exposure to mining, commercial logging, and climate change: A Tasmanian case study

Formal protected areas are a critical conservation measure so long as their tenure is defined and secure and they are well managed. Protected areas in developed countries are assumed to meet these criteria and therefore have not attracted the level of attention given to the adequacy of protected areas in developing countries. We investigate this assumption using as a case study the southern temperate rainforests of Tasmania, Australia. We examine the extent to which these rainforests are protected from potential exposure to mining, commercial logging and climate change. We analyse the tenure of Tasmania's rainforests and identify the protected area categories that prohibit or allow mining or logging. We also model the potential distribution of Nothofagus cunninghamii, a dominant rainforest canopy tree species, to future climate and compare this with modelled current and future forest fire danger index. Results showed that 90% of the total area of Tasmanian rainforest (715,773ha−1) is in a reserve. However, the area of rainforest in reserves secured from mining and/or commercial logging is only 47% (335,863ha−1) as 43% (308,897ha−1) is in a reserve category where these land uses are permitted. The protected area category with the highest level of protection, prohibiting all mining and logging, is the Tasmanian Wilderness World Heritage Area which encompasses 325,920ha−1 of temperate rainforest. During a recent legislative review, 66,012ha−1 of rainforest in protected areas was downgraded to a reserve category that permits logging or mining. A key conservation instrument therefore is the Management Plan for the World Heritage Area as it overrides land use activities otherwise permitted including the 21,257ha−1 which is on a State-defined land tenure that allows for logging or mining. Climate change impacts, as modelled, suggest the main conservation challenges are in maintaining the integrity of the remaining intact rainforest blocks and better managing ignitions from lightning strikes and arsonists in the coniferous and alpine rainforests. Allowing structural degradation and fragmentation to intact rainforest blocks will reduce their capacity to buffer meso-climatic variability and resist fire events thereby undermining their ecosystem integrity. Noting that Aichi Target 11 includes the requirement that reserves are effectively managed, our case study highlights that assessing the effectiveness of a reserve system is not necessarily a straightforward matter as governance systems and regulatory frameworks involve a mix of international obligations, national and subnational policies and statutes, along with other agreements, administrative arrangements and plans of management, which can provide for a range of land use activities and be subject to modification over time.

Effects of climate on the size of wildfires in the Eucalyptus camaldulensis forests and the dry lands of the Riverina Bioregion, Australia

Wildfire affects ecosystem processes, threatens human lives and causes economic loss. In forested wetlands of inland Australia, a better understanding of fire regime pattern is required given the dense population of fire-sensitive species such as Eucalyptus camaldulensis (river red gum). This study aims to investigate wildfire characteristics and the effects and relative contributions of ambient weather and antecedent rainfall on fire size in forested wetlands and the surrounding dry lands of the Riverina bioregion, New South Wales, Australia. Fires burned entirely in forested wetlands (FEW), fires burned partly in forested wetlands (FPW) and fires that did not burn in forested wetlands (FNW), are progressively incorporated into statistical analyses, in order to explore the change of effects and contributions of factors as the fuel type changes from litter/grass to shrub/grass.Forested wetlands and dry lands experience summer and spring-summer fire regime, respectively. Higher cumulative rainfall conditions of the 6th, 13-14th and 17-18th months before fire drive a larger size of FEW, while a number of factors representing the cumulative rainfall after the 18th month before fire positively affect fire size when FPW and FNW are being modelled. A larger fire extent is also driven by severer ambient weather conditions. Forest Fire Danger Index (FFDI) is more powerful in explaining the size of FEW, while the daily temperature becomes more effective when FPW and FNW are gradually incorporated, which supports the use of respective factors as indicators of fire danger in the two diverse ecosystems.Antecedent rainfall factors are more powerful than ambient weather in explaining the size of FEW, while they become less important when the surrounding lands are incorporated. After a high level of rainfall and under similar ambient weather conditions, priority of fire risk management may be given to forested wetlands since fire size is more sensitive to biomass accumulation in forested wetlands than in the adjacent dry lands.

Attributing extreme fire risk in Western Canada to human emissions

Canada is expected to see an increase in fire risk under future climate projections. Large fires, such as that near Fort McMurray, Alberta in 2016, can be devastating to the communities affected. Understanding the role of human emissions in the occurrence of such extreme fire events can lend insight into how these events might change in the future. An event attribution framework is used to quantify the influence of anthropogenic forcings on extreme fire risk in the current climate of a western Canada region. Fourteen metrics from the Canadian Forest Fire Danger Rating System are used to define the extreme fire seasons. For the majority of these metrics and during the current decade, the combined effect of anthropogenic and natural forcing is estimated to have made extreme fire risk events in the region 1.5 to 6 times as likely compared to a climate that would have been with natural forcings alone.

Forest Fire Danger Assessment Using SPMD-Model of Computation for Massive Parallel System

A new approach to forecast forest fire danger using parallel computing operations in the computing systems with parallel architecture is the subject of this article. The parallel realization of the deterministic-probabilistic approach to estimate forest fire danger using the SPMD calculation model is presented. The parallel program is implemented using C programming language of high level with the Message Passing Interface (MPI). The computing experiments have been carried out by the Skif Cyberia multiprocessor cluster. The information on efficiency estimation and calculation acceleration is presented for this approach. The approach has been tested in the territory of the Timiryazevskiy forestry in Tomsk region (Russia). The typical results of forest fire danger estimation in the controlled forested territory are presented.

Spatiotemporal variation in forest fire danger from 1996 to 2010 in Jilin Province, China

We evaluated the spatial and temporal patterns of forest fires in two fire seasons (March to June and September to November) from 1996 to 2010 in Jilin Province, China, using the Canadian Forest Fire Weather Index System. Fire data were obtained from the Provincial Fire Agency, and historical climate records of daily weather observations were collected from 36 weather stations in Jilin and its neighboring provinces. A linear regression model was used to analyze linear trends between climate and fire weather indices with time treated as an independent variable. Correlation analysis was used to detect correlations between fire frequency, areas burned, and fire weather indices. A thin-plate smooth spline model was used to interpolate the point data of 36 weather stations to generate a surface covering the whole province. Our analyses indicated fire frequency and areas burned were significantly correlated with fire weather indices. Overall, the Canadian Forest Fire Weather Index System appeared to be work well for determining the fire danger rating in Jilin Province. Also, our analyses indicated that in the forthcoming decades, the overall fire danger in March and April should decrease across the province, but the chance of a large fire in these months would increase. The fire danger in the fall fire season would increase in the future, and the chance of large fire would also increase. Historically, because most fires have occurred in the spring in Jilin Province, such a shift in the future fire danger between the two fire seasons would be beneficial for the province’s fire management.

Cffdrs: an R package for the Canadian Forest Fire Danger Rating System

IntroductionThe Canadian Forest Fire Danger Rating System (CFFDRS) is a globally known wildland fire risk assessment system, and two major components, the fire weather index system and the fire behavior prediction system, have been extensively used both nationally and internationally to aid operational wildland fire decision making.MethodsIn this paper, we present an overview of an R package cffdrs, which is developed to calculate components of the CFFDRS, and highlight some of its functionality. In particular, we demonstrate how these functions could be used for large data analysis.Results and DiscussionWith this cffdrs package, we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior. This is the first time that all relevant CFFDRS methods are incorporated into the same platform, which can be accessed by both the management and research communities.

Forest fire danger, life satisfaction and feelings of safety: evidence from Australia

Employing data from the Household, Income and Labour Dynamics in Australia survey and the McArthur Forest Fire Danger Index, this study tests: (1) the association between forest fire danger and an individual’s life satisfaction; (2) the association between forest fire danger and an individual’s feeling of safety; and (3) whether the association between forest fire danger and an individual’s life satisfaction is explained by feelings of safety. Further, this study employs the experienced preference method to estimate, in monetary terms, the psychological costs associated with forest fire danger. We find negative and significant associations between life satisfaction and forest fire danger, as well as between forest fire danger and feelings of safety. When feelings of safety are included in the life satisfaction regression, however, the forest fire danger variable is no longer statistically significant – suggesting that the link between forest fire danger and life satisfaction can be largely explained by an individual’s feelings of safety. The experienced preference method yields an implicit willingness-to-pay of $10 per year to avoid a one unit increase in the spatially weighted average of the average daily value of the Fire Danger Index over the previous 12 months.