A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces

The use of GIS for the development of a fully embedded predictive fire model

Fire is very important for maintaining balance in the ecosystems and is used by fire management across the world to regulate growth of vegetation in natural conservation areas. However, improper management of fire may lead to hazardous behaviour. Fire modelling tools are implemented to provide fire managers with a platform to test and plan fire management activities. Fire modelling occurs in two parts: fire behaviour models and fire spread models, where fire behaviour models account for the behaviour of fires that is used in fire spread models to model the propagation of a fire event. Since fire is a worldwide phenomenon a number of fire modelling approaches have been developed across the world. Most existing fire models only model either fire behaviour or fire spread, but not both, hence full integration of fire models into GIS is not completely implemented. Full integration of environmental modelling in GIS refers to the case where an environmental model such as a fire model is implemented within a GIS environment, without requiring any transfer of data from other external environments. Most existing GIS based fire spread models account for fire propagation in the direction of prevailing winds (or defined fire channels) as opposed to full fire spread in all directions. The purpose of this study is to illustrate the role of GIS in fire management through the development of a fully integrated, predictive, wind driven, surface fire model. The fire model developed in this study models both the risk of fire occurring (fire behaviour model), and the propagation of a fire in case of an ignition incident (fire spread model), hence full integration of fire modelling in a GIS environment. The fire behaviour model is based on prevailing meteorological conditions, the type of vegetation in an area, and the topography. The spread of a fire in this model is determined by the transfer of heat energy and rate of spread of fire, and is developed based on the Cellular Automata (CA) modelling approach. This model considers the spread of fire in all directions instead of the forward wind direction only as is the case in most fire spread models. The fire behaviour model calculates fire intensity and rate of spread which are used in the fire spread model, hence demonstrating the full integration of fire modelling in GIS. No external data exchange with the model occurs except for acquisition of input data such as measured values of environmental conditions. v This cellular automata based fire spread model is developed in the ArcGIS ModelBuilder geoprocessing environment, and requires the development of a custom geoprocessing function tool to facilitate the fast and effective performance of the model. The test study area used in this research is the Kruger National Park because of frequent fire activity that occurs in the park, as a result of management activities and accidental fires, and also because these fires are recorded by park fire ecologists. Validation of the model is achieved by comparison of simulated fire areas after a certain period of time with known location of the fire at that particular time. This is achieved by the mapping of fire scars and active fire areas acquired from MODIS Terra and Aqua images, fire scars are also acquired from the Kruger National Park Scientific Services. Upon evaluation, the results of the fire model show successful simulation of fire area with respect to time. The implementation of the model within the ArcGIS environment is also performed successfully. The study thus concludes that GIS can be successfully used for the development of a fully integrated (embedded) fire model

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Modélisation de répartition d’espèces aviaires et de feux en forêt boréale du Québec dans un contexte de changement climatique

Les changements climatiques prennent une importance grandissante dans l’étude des phénomènes spatiaux à grande échelle. Plusieurs experts affirment que les changements climatiques seront un des principaux moteurs de changement écologique dans les prochaines décennies et que leurs conséquences seront inévitables. Ces changements se manifesteront sur le milieu physique par la fonte des calottes glaciaires, le dégel du pergélisol, l’instabilité des versants montagneux en zone de pergélisol, l’augmentation de l’intensité, de la sévérité et de la fréquence des événements climatiques extrêmes tels les feux de forêt. Les changements climatiques se manifesteront aussi sur le milieu biologique, tel la modification de la durée de la saison végétative, l’augmentation des espèces exotiques invasives et les changements dans la distribution en espèces vivantes. Deux aspects sont couverts par cette étude : 1) les changements dans la répartition spatiale de 39 espèces d’oiseaux et 2) les modifications dans les patrons spatiaux des feux, en forêt boréale québécoise, tous deux dans l’horizon climatique de 2100. Une approche de modélisation statistique démontre que la répartition spatiale des oiseaux de la forêt boréale est fortement liée à des variables bioclimatiques (R2adj = 0.53). Ces résultats permettent d’effectuer des modélisations bioclimatiques pour le gros-bec errant et la mésange à tête noire quivoient une augmentation de la limite nordique de distribution de l’espèce suivant l’intensité du réchauffement climatique. Finalement, une modélisation spatialement explicite par automate cellulaire permet de démontrer comment les changements climatiques induiront une augmentation dans la fréquence de feux de forêt et dans la superficie brûlée en forêt boréale du Québec.Climate is getting more important in the study of broad-scale spatial phenomena. Experts agree on the fact that climate change will likely be one of the main drivers of ecological change in the upcoming decade and that its consequences are inevitable. These chances induce consequence on the physical aspects, by ice caps melting, permafrost thawing, slope and soil instability in mountainous permafrost areas and increase of intensity, severity and frequency of extreme weather events such as wildland fires. Moreover, climate chance also causes impacts on the biological aspects, with modification in the growing season, increase in exotic invasive species, and changes in spatial distribution of butterfly and bird species. Two of these aspects are covered in this study: 1) changes in the spatial distribution of 39 bird species and 2) modifications in spatial patterns of wildland fires, in the boreal forest of Quebec, up to year 2100. A statistical modelling approach showed that the spatial distribution of boreal birds is strongly linked to bioclimatic variables (53%). These results enables the bioclimatic modelling of two bird species, the evening grosbeak and black-capped chickadee. In both cases, the model shows an increase of the northernmost limit of distribution of each species following the rate of climate change. Finally, a spatially-explicit cellular automata model showed that climate change will likely induce an increase in wildfire frequency and total area burned for the boreal forest of Quebec

An intelligent cellular automaton scheme for modelling forest fires

Forest fires have devastating consequences for the environment, the economy and human lives. Understanding their dynamics is therefore crucial for planning the resources allocated to combat them effectively. In a world where the incidence of such phenomena is increasing every year, the demand for efficient and accurate computational models is becoming increasingly necessary. In this study, we perform a revision of an initial proposal which consists of a two-dimensional propagation model based on cellular automata (2D-CA), which aims to understand the dynamics of these phenomena. We identify the key theoretical weaknesses and propose improvements to address these limitations. We also assess the effectiveness and accuracy of the model by evaluating improvements using real forest fire data (Beneixama, Alicante 2019). Moreover, as a result of the theoretical modifications performed, we introduce a novel intelligent architecture that seeks to capture relationships between system cells from the data. This new architecture has the ability to advance our understanding of forest fire dynamics, contributing to both the evaluation of existing protocols and more efficient firefighting resource management.This research is funded by Generalitat Valenciana, project AICO/2021/331

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster’s impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms