Forest fire spreading: A nonlinear stochastic model continuous in space and time

Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.

Wildfires are an increasing threat in Mediterranean regions, where extreme fire weather and long-term fuel accumulation are driving more frequent and severe events. In this context, fast and reliable fire spread simulations are essential to support both risk mitigation planning and real-time emergency management. PROPAGATOR is a stochastic Cellular Automata (CA) wildfire spread simulator designed to generate ensemble-based fire spread forecasts. The model, currently available as both an online application and open-source software, operates within a raster-based framework in which each cell is described by static attributes (e.g. fuel type, topography) and dynamic drivers (e.g. wind, fuel moisture). Fire propagation is modelled through a stochastic contamination process between burning and unburned cells, allowing the production of probabilistic maps of fire spread, as well as statistics on rate of spread and fireline intensity. PROPAGATOR also includes the capability to simulate the spotting phenomenon and suppression actions such as water drops or firebreak construction, making it suitable for both operational decision support during active fires and pre-event risk mitigation analyses. A current limitation of operational applications of PROPAGATOR is its focus on surface fire propagation, with no explicit representation of vertical fuel structure or transitions to crown fire. Crown fires, however, are characterized by higher spread rates, greater energy release, and increased unpredictability, with major implications for suppression effectiveness and ecological impacts. To address this limitation, an enhanced version of PROPAGATOR has been developed by extending the model to a quasi-three-dimensional (2.5D) representation of fuels, enabling the simulation of crown fire processes within the stochastic CA framework. The proposed Crown Fire Module relies on established empirical and semi-empirical formulations for crown fire initiation and spread that are compatible with a cellular automata approach. Crown fire initiation is governed by surface fireline intensity and crown base height, while crown fire rate of spread depends primarily on canopy bulk density and fire behaviour. These mechanisms have been integrated into the propagation rules of PROPAGATOR, allowing dynamic transitions between surface and crown fire behaviour within a probabilistic modelling framework. The implementation of these processes requires detailed information on both surface and canopy fuel structure and characteristics, which remains challenging at operational scales. To address this issue, we investigated the use of UAV-based LiDAR remote sensing to derive key fuel structure parameters using semi-automatic algorithms available in the literature. This approach offers a balance between spatial detail and areal coverage that is suitable for operational wildfire applications. A pilot study conducted in the Venafro area (Molise, Italy), based on a past wildfire event with a comprehensive dataset describing fire evolution, provided high-resolution inputs to test the enhanced model. By explicitly simulating surface-to-crown fire transitions, the upgraded version of PROPAGATOR aims to improve decision support for wildfire risk management, supporting applications ranging from fuel treatment planning to operational response under extreme fire weather conditions.

The aim of this paper is to propose a more practical mountain fire spread model for fire behavior prediction and management in Southwest forest area of China. These areas are covered mainly with spatial heterogeneous flammable forest and are characterized by undulating terrain and steep slopes. This model can produce more accurate fire propagation maps by combining CA (Cellular Automaton) framework with Wang Zhengfei fire physical velocity model in fine scale. Considering the inherent uncertainties of fire propagation, the model has been built on multi-dimension geophysical and environmental components and also sound knowledge of fire spread physical mechanism. Regarding small fuel patches as spatial homogenous cells, this approach makes it easier to generate higher level complex fire behavior maps from CA simple local rules and local behavior integrated with high resolution vegetation images, fine scale terrain maps and surface wind field. Because the model focuses primarily on the study of surface fire front propagation behavior, it attempts to simplify complex fuel modeling. Additionally, this Wang-Geophysical-CA model is able to analyze the time series spatial pattern of fire-front spread and model local behavior instead of the final fire spread pattern of the conventional approach. In this work, not only single influence verification tests have been made, but also simulation tests with multiple influences are carried out to demonstrate the capability of the model with fine scale vegetation maps, surface wind field, terrain, moisture content and man-made structures. Consequently, it is believable that the model predictions are in good agreement with experimental data for steady-state fire simulation. The proposed model helps to gain a greater understanding of the fire front spread local behavior and can quickly generate a sequence of complex fire front contours. It enables local managers to plan practical fire prevention activities in Southwest forest area of China as well as improve fire management skills, and will enhance the effectiveness of fire fighting strategies.

Forest fires are of common occurrence all over the world, causing severe damages to valuable natural environment and loss of human lives. In order to reduce the damages by forest fires, it is useful to utilize a system, which can predict the occurrence of forest fires and the spread of fires. Well known is a system in USA, called NFDRS to predict forest fire occurrence and FARSITE to predict fire growth, based on the fire weather information taken from a network, combined with forest fuel conditions and land topography data, and processed by an algorithm to generate the various fire danger indices. In Japan the number of forest fires is roughly 3,000 per year, which is 1/30 times compared with USA, and there are very few fires exceeding 1000 ha burnt area, hence there has existed scant demand for this type of intelligent system. Although recently there is an increasing demand for such a system in Japan, the US system for forest-fire prediction is however not applicable to Japan, since the forest topology and weather conditions between Japan and USA are far different. Moreover, many fire weather stations have been installed in the US forests, but in Japan no such fire weather stations are installed in forests. Thus, as a first step to develop an intelligent system for Japan, we have analyzed the fundamentals of forest fire danger rating and the fire spread, based on the weather data and other information on forest fires. The objective of this study is to examine how the fundamentals, based on analyzing the past fire occurrences and CFD simulations particularly on “Katunuma Fire”, can predict the occurrence of forest fires and the spread of forest fires.

A numerical model is developed to describe the dendritic growth of multicomponent aluminium alloys, based on a coupled deterministic continuum mechanics heat and species transfer model and a stochastic localized growth model that takes into account the undercooling temperature, curvature, kinetic, and thermodynamic anisotropy. The stochastic model receives temperature and concentration information from the deterministic model and the deterministic heat and species diffusion equations receive the solid fraction information from the stochastic model. The heat and species transfer models are solved on a regular grid by the standard explicit Finite Difference Method (FDM). The dendritic growth model of multicomponent alloy [1,2] is solved by a novel Point Automata (PA) approach [3,4] where the regular cells of the Cellular Automata (CA) method are replaced by the randomly distributed points and neighborhood configuration, similar as appears in meshless methods. The PA method was developed in order to circumvent the mesh anisotropy problem, associated with the classical CA method. The present paper extends our previous developments of Pa method to multicomponent alloys. A comparison of the results, obtained by the PA and CA method is shown for Al-5.3% Zn-2.35% Mg-1.35% Cu-0.5% alloy.

Fire spreading is one of the visualization techniques used for re-enacting or envisions the fire incidents for conducting the post-incidents’ responses and analysing the incidents for post-mortem purposes. There are several current researches on the fire spreading incidents that involve the construction of fire spreading simulation which has focusing on the fire development, smoke control, the prediction of temperature distribution during the fire spreading, emergency response’s plans and post-fire damage assessment. However, there are more features need to be explored in the fire spreading simulation and also the pedestrians movement of the affected incident’s area for the future space design development, arrangement and structural improvement that are impactful towards human safety and also useful for the justification and prediction on the pedestrian survival rate during any panic situations. Hence, this research has focusing on the features of realistic scaling of the spatial layout and implementing the Cellular Automata (CA) approach for imitating the near-realistic pedestrian self-organizing movement and fire spreading characteristics at the microstructure level for designing the heat map of the affected area to show the clogging region in the spatial layout while constructing a reliable prediction on the pedestrian survival rate. This clogging region mapping will be useful for finding the existing issues that lead towards high casualties. Based on the experiments and observations, the heat map of the affected area showed the heavy congestions happened specifically near to the ingress/ egress points and narrow pathways that had affected the pedestrian flow rate and caused the 75% of the 352 pedestrians in the spatial layout to burn and die during the fire simulation by unintentionally taking an extra of 43.85 seconds more than the total fire spreading time (13.42 seconds) to evacuate from the closed area building.

It is important to examine the behavior of forest fires and city fires to mitigate the property damages and victims by fires. There have been many previous studies on forest fires where the fire spreading patterns were investigated, utilizing artificial satellite pictures of forest fires, together with the use of corresponding weather data and GIS data. On the other hand, large area city fires are very scarce in the world, particularly in modern cities where high-rise concrete buildings are constructed with sufficient open spaces. Thus, the examples of city fires to be referred are few and detailed investigations of city fires are limited. However, there have still been existing old cities where traditional houses built with flammable material such as wood, maybe historically important, only separated with very small open spacing. Fires may freely spread in those cities, once a big earthquake happens there and then water supply for the fire brigade is damaged in the worst case along with the effect of strong wind. There are some fundamental differences between the forest fires and city fires, as the fuel may distribute either continuously or discretely. For instance, in forest fires, the dead fallen leaves, dry grasses and trees are distributed continuously on the ground, while the wooden houses in cities are discretely distributed with some separation of open spacing, such as roads and gardens. Therefore, the wooden houses neighboring the burning houses with some separation are heated by radiation and flames to elevate the temperatures, thus causing the ignition, and finally reaching a large city fire. The authors have studied the forest fire spread and are planning to start a laboratory experiment of city fire spreading. In the preliminary investigation, a numerical study is made to correlate with the laboratory experiment of city fire propagation, utilizing the three-dimensional CFD simulations. Based on the detailed experimental analysis, the authors are attempting to modify the three dimensional CFD code to predict the forest fires and city fires more precisely, taking into account the thermal heating and ignition processes. In this study, some fundamental information on the city fire propagation has been obtained, particularly to know the safe open spacing distances between the houses in the cities and also the wind speed.

Simulation of forest fire fronts using cellular automata

Crown fires create broad-scale patterns in vegetation by producing a patch mosaic of stand age classes, but the spread and behavior of crown fires also may be constrained by spatial patterns in terrain and fuels across the landscape. In this review, we address the implications of landscape heterogeneity for crown fire behavior and the ecological effects of crown fires over large areas. We suggest that fine-scale mechanisms of fire spread can be extrapolated to make broad-scale predictions of landscape pattern by coupling the knowledge obtained from mechanistic and empirical fire behavior models with spatially-explicit probabilistic models of fire spread. Climatic conditions exert a dominant control over crown fire behavior and spread, but topographic and physiographic features in the landscape and the spatial arrangement and types of fuels have a strong influence on fire spread, especially when burning conditions (e.g., fuel moisture and wind) are not extreme. General trends in crown fire regimes and stand age class distributions can be observed across continental, latitudinal, and elevational gradients. Crown fires are more frequent in regions having more frequent and/or severe droughts, and younger stands tend to dominate these landscapes. Landscapes dominated by crown fires appear to be nonequilibrium systems. This nonequilibrium condition presents a significant challenge to land managers, particularly when the implications of potential changes in the global climate are considered. Potential changes in the global climate may alter not only the frequency of crown fires but also their severity. Crown fires rarely consume the entire forest, and the spatial heterogeneity of burn severity patterns creates a wide range of local effects and is likely to influence plant reestablishment as well as many other ecological processes. Increased knowledge of ecological processes at regional scales and the effects of landscape pattern on fire dynamics should provide insight into our understanding of the behavior and consequences of crown fires.

Variations in global patterns of burning and fire regimes are relatively well measured, however, the degree of influence of the complex suite of biophysical and human drivers of fire remains controversial and incompletely understood. Such an understanding is required in order to support current fire management and to predict the future trajectory of global fire patterns in response to changes in these determinants. In this study we explore and compare the effects of four fundamental controls on fire, namely the production of biomass, its drying, the influence of weather on the spread of fire and sources of ignition. Our study area is southern Australia, where fire is currently limited by either fuel production or fuel dryness. As in most fire-prone environments, the majority of annual burned area is due to a raelatively small number of large fires. We train and test an Artificial Neural Networks ability to predict spatial patterns in the probability of large fires (>1,250 ha) in forests and grasslands as a function of proxies of the four major controls on fire activity. Fuel load is represented by predicted forested biomass and remotely sensed grass biomass, drying is represented by fraction of the time monthly potential evapotranspiration exceeds precipitation, weather is represented by the frequency of severe fire weather conditions and ignitions are represented by the average annual density of reported ignitions. The response of fire to these drivers is often non-linear. Our results suggest that fuel management will have limited capacity to alter future fire occurrence unless it yields landscape-scale changes in fuel amount, and that shifts between, rather than within, vegetation community types may be more important. We also find that increased frequency of severe fire weather could increase the likelihood of large fires in forests but decrease it in grasslands. These results have the potential to support long-term strategic planning and risk assessment by fire management agencies.

Forest fire is one of the major hazards causing destruction to biodiversity, environment and humans in the Taradevi forest of Himachal Pradesh (India). The study was carried out for forest fire spread analysis and loss assessment using simulation modeling techniques. Knowledge of the factors which includes forest types, canopy cover, meteorological status, topographic feature accelerating forest fire were taken into considerations. The parameters derived from remote sensing data and Geographical Information System (GIS) were used to generate input files for forest fire simulation modeling using FARISTE. Finally, a fire spread maps and fire areas were predicted in this simulation, where relative importance is given to each theme built based on GIS and climate parameters. The study aims to develop a simulation method to find out fire spread and direction according to the climate data like wind direction, wind speed, rainfall, forest fuel type & density, canopy cover and other maps required for it. Findings of the research are helpful in development of forest fire management. Fast and appropriate direction will be used by the management to stop the spread of fire effectively. It helps to provides effective means for protecting forests from fires as well as to formulate appropriate methods to control and manage forest fire damages and its spread.

BackgroundAn approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events.ResultsUncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial–temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected.ConclusionThis information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-016-2842-9) contains supplementary material, which is available to authorized users.

Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model

Comparison of deterministic and stochastic SIS and SIR models in discrete time

The paper presents a survey of developed multi-component diffusion models. Stochastic and continuous PDE models for multi-component diffusion problems are studied by performing a sequence of numerical simulations. The stochastic models are based on Cellular Automata with interchange and block-rotation model. The multi-component particles are either interchanged or rotated clockwise or counter-clockwise with prescribed probability. The diffusion coefficient is tuned by changing the probabilities of execution of particular rules. The continuous PDE models are based on classical variational formulations solved by the self-adaptive hp Finite Element Method. Numerical results are compared and discussed.