One crucial mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon that occurs when embers are transported over large distances. Fire-spotting speeds up the rate of spread and starts new ignitions that can jeopardise firefighting operations. Unfortunately, operational fire-spread simulators may not account for spotting events, thus overlooking the harmful consequences associated with this phenomenon. In this work, three fire spotting parametrisations are integrated in the operational wildfire simulator based on Cellular Automata (CA). RandomFront, a physics-based parametrisation of fire-spotting, is tested for the first time in the context of CA simulators. RandomFront is compared with other two parametrisations already adopted in CA based simulators, those by Alexandridis and co-authors and by Perryman and collaborators. A wildfire occurred in the summer of 2021 in the municipality of Campomarino (Molise, Italy), and where spotting effects were clearly reported, is used as a case study. This case study, featuring evident airborne transport of firebrands, paves the way for a framework for comparing parameterised spotting models used in operational scenarios. RandomFront produced a more complex burning probability pattern than the other parametrisations and it predicted a higher probability of burning in the zone mainly affected by the fire-spotting.

中文翻译：

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基于元胞自动机的可操作野火模拟器中的火点建模：比较研究

野火蔓延的一个关键机制是所谓的火斑：当余烬长距离运输时发生的一种随机现象。火灾发现会加快蔓延速度并引发新的火灾，从而危及消防行动。不幸的是，可操作的火势蔓延模拟器可能无法解释发现事件，从而忽视了与这种现象相关的有害后果。在这项工作中，三个火灾定位参数被集成到基于元胞自动机 (CA) 的可操作野火模拟器中。 RandomFront 是一种基于物理的火点参数化方法，首次在 CA 模拟器中进行了测试。 RandomFront 与基于 CA 的模拟器中已经采用的其他两种参数化（由 Alexandridis 和合著者以及 Perryman 和合作者采用的参数化）进行了比较。本文以 2021 年夏季坎波马里诺市（意大利莫利塞）发生的一场野火为案例研究，现场清楚地报告了火灾的影响。本案例研究以明显的火药空中传播为特色，为比较作战场景中使用的参数化定位模型的框架铺平了道路。 RandomFront 产生了比其他参数更复杂的燃烧概率模式，并且它预测主要受火斑影响的区域发生燃烧的概率更高。