Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ecology and human life in Australia. This study proposes a prototype of fire propagation prediction as an extension of preceding research; this system is called “Cloud computing based bushfire prediction”, the computational performance of which is expected to be about twice that of the traditional client-server (CS) model. As the first step in the modelling approach, this prototype focuses on the prediction of fire propagation. The direction of fire is limited in regular grid approaches, such as cellular automata, due to the shape of the uniformed grid, while irregular grids are freed from this constraint. In this prototype, fire propagation is computed from a centroid regardless of grid shape to remove the above constraint. Additionally, the prototype employs existing fire indices, including the Grassland Fire Danger Index (GFDI), Forest Fire Danger Index (FFDI) and Button Grass Moorland Fire Index (BGML). A number of parameters, such as Digital Elevation Model (DEM) and forecast weather data, are prepared for use in the calculation of the indices above. The fire study area is located around Lake Mackenzie in the central north of Tasmania where a fire burnt approximately 247.11 km 2 in January 2016. The prototype produces nine different prediction results with three polygon configurations, including Delaunay Triangulation, Square and Voronoi, using three different resolutions: fine, medium and coarse. The Delaunay Triangulation, which has the greatest number of adjacent grids among three shapes of polygon, shows the shortest elapsed time for spread of fire compared to other shapes. The medium grid performs the best trade-off between cost and time among the three grain sizes of prediction polygons, and the coarse size shows the best cost-effectiveness. A staging approach where coarse size prediction is released initially, followed by a medium size one, can be a pragmatic solution for the purpose of providing timely evacuation guidance.
Wildfires are not only a natural part of many ecosystems, but they can also have disastrous consequences for humans, including in Australia. Rugged terrain adds to the difficulty of predicting fire behavior and fire spread, as fires often propagate contrary to expectations. Even though fire models generally incorporate weather, fuels, and topography, which are important factors affecting fire behavior, they usually only consider the surface wind; however, the more elevated winds should also be accounted for, in addition to surface winds, when predicting fire spread in rugged terrain because valley winds are often dynamically altered by the interaction of a layered atmosphere and the topography. Here, fire spread in rugged terrain was examined in a case study of the Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 in southern Tasmania, Australia and burnt approximately 637.19 km2. Firstly, the number of conducive wind structures, which are defined as the combination of wind and temperature layers likely to result in enhanced surface wind, were counted by examining the vertical wind structure of the atmosphere, and the potential for above-surface winds to affect fire propagation was identified. Then, the multiple fire propagations were simulated using a new fire simulator (Prototype 2) motivated by the draft specification of the forthcoming new fire danger rating system, the Australian Fire Danger Rating System (AFDRS). Simulations were performed with one experiment group utilizing wind fields that included upper-air interactions, and two control groups that utilized downscaled wind from a model that only incorporated surface winds, to identify the impact of upper air interactions. Consequently, a detailed analysis showed that more conducive structures were commonly observed in the rugged terrain than in the other topography. In addition, the simulation of the experiment group performed better in predicting fire spread than those of the control groups in rugged terrain. In contrast, the control groups based on the downscaled surface wind model performed well in less rugged terrain. These results suggest that not only surface winds but also the higher altitude winds above the surface are required to be considered, especially in rugged terrain.
Although mountain areas account for approximately one fifth of the terrestrial surface, there has been less research focused on fire in these areas compared to lowlands. Mountain fires have distinct behavior due to dynamic winds interacting with the terrain, which can influence the fireline intensity and propagation. For the sake of fire safety of fire crews, it is essential to know how difficult to control the fire is in the mountain regions, with fireline intensity providing a useful indicator of risk and suppressibility. We studied one of the major disasters, wildfire, in Australia in such a highland by using the Great Pine Tier Fire, which occurred 15th January in 2019, ending up burning approximately 511.86km2. Weather and fire intensity at pseudo weather stations located at key points of fire progression were analyzed by wind vector maps and numerical weather model vertical sounding (NWMVS). Fire propagation was then simulated in Prototype 2, a fire simulator capable of detecting the potential for lateral fire channeling (LFC), and simulating fireline intensity using Australian vegetation sub-models. We found that the synoptic wind appeared to be modified by the interaction with the terrain in windward and the fire intensified the most in its leeward. In practice, the fire moved out of the valley axis and up its sidehill by following the wind which had been modified by local vertex of the curved valley axis before reaching this location.
Background: We studied Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 and burnt more than 637.19 km2 in southern Tasmania, Australia. Aims: We focused on fire weather, such as identification of dynamic wind and vegetation type, in a valley of the study area. Methods: We employed two methods: numerical weather model vertical sounding (NWMVS) and the use of a fire simulator, to quantify and examine the contribution of dynamic winds to fire behaviour. The NWMVSs allow rapid diagnosis of changes in wind, temperature, dew point temperature and cloud coverage. Prototype 2 is a fire simulator based on the specification of Australian Fire Danger Rating System (AFDRS). Key results: We found fires to be guided by terrain-forced channelling primarily and by downslope wind conditionally in the valleys. In addition, the fire intensity periodically changed with the magnitude of surface wind, in buttongrass moorland, in which the fire often smoulders, during the fire period according to the satellite image. Conclusions and Implications: Therefore, there should be caution for not only terrain and dynamic wind but also vegetation type during fire spread in rugged terrain.
Mountain fire can become more complex than fires at lower elevation due to the complex interaction of fire, topography, and weather. The Gell River Fire in Tasmania, Australia occurred in rugged terrain where there are abundant fire sensitive vegetation communities, as well as the presence of infrastructure including high-voltage transmission lines. The fire began at the end of December 2018 and lasted a few months, with a final burnt area of approximately 350km2 despite significant fire suppression effort. The fire was investigated by employing wind vector maps, numerical weather model vertical sounding charts (NWMVS) and Prototype 2, which is an integrated fire simulator and can detect lateral fire channeling (LFC). Our analysis of the fire found its spread was likely to be introduced into a valley by forced channeling (FC), which is modified synoptic wind, and showed rapid spread in the valley. The simulated fire also showed wider spread than the observed data in the valley, with the simulated fire impacting highly sensitive vegetation communities on the fringes of the valley. This alludes to some potential conclusions: (1) The loss of fire sensitive vegetation would have increased if fire suppression activity had not been conducted. (2) Spotting fires could be produced by LFC because these spotting fire would allow spreading fire in a shorter period. (3) Heterogeneity of vegetation, such as combination of buttongrass and forest, could help carry fire rapidly in the valley with LFC. Fire can propagate faster in buttongrass than in forests while the forests allow the spotting fire.
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