Sub-canopy windspeed is a critical input variable in fire simulation modelling, having a strong effect on the predicted rate of spread (ROS). In vegetated landscapes, windspeed reduction occurs due to the structural properties of vegetation, with the canopy height and forest density being key structural attributes driving this effect. Wind reduction factors (WRFs) are used to represent this phenomenon in fire behaviour modelling. Significant variability in WRFs exist both laterally and vertically; however, this variation has been poorly represented in operational models for two key reasons: i) a lack of an operational-scale spatial dataset to characterise the key forest attributes and parameterise a WRF model spatially and vertically; and ii) the lack of a method to integrate these spatial parameters into an operational WRF model. We address these challenges by developing a novel WRF model using spatial inputs from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR dataset. This model relies on the Plant Area Index (PAI) vertical distribution and canopy height derived from GEDI to predict WRFs at different heights within the forest profile and across landscapes. Model validation is conducted using observed vertical WRF profile across 11 sites. This model represents an advancement in operational WRF modelling by explicitly integrating large-scale spatial datasets that characterise vertical forest structure. It demonstrates, using an Australian case study, the feasibility of using GEDI data to model WRFs operationally, providing spatially and vertically explicit predictions. This proof-of-concept establishes a scalable method to bridge critical gaps in WRF modelling for wildfire prediction.

中文翻译：

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星载 LiDAR 衍生的高度可变森林风力减损因子 （WRF） 用于改进野火蔓延建模

树冠下风速是火灾模拟建模中的关键输入变量，对预测蔓延速率 （ROS） 有很大影响。在植被景观中，由于植被的结构特性，风速降低，而树冠高度和森林密度是驱动这种效果的关键结构属性。在火灾行为建模中，风力减弱系数 （WRF） 用于表示这种现象。WRF 在横向和纵向上都存在显著的变异性;然而，这种变化在业务模型中的代表性很差，主要有两个原因：i） 缺乏业务规模的空间数据集来描述关键森林属性并在空间和垂直上参数化 WRF 模型;ii） 缺乏将这些空间参数整合到可作的 WRF 模型中的方法。我们通过使用来自全球生态系统动力学调查 （GEDI） 星载 LiDAR 数据集的空间输入开发一种新的 WRF 模型来应对这些挑战。该模型依赖于 GEDI 得出的植物面积指数 （PAI） 垂直分布和冠层高度来预测森林剖面内和景观中不同高度的 WRF。使用在 11 个站点观察到的垂直 WRF 剖面进行模型验证。该模型通过显式集成表征垂直森林结构的大规模空间数据集，代表了业务 WRF 建模的进步。它使用澳大利亚案例研究展示了使用 GEDI 数据对 WRF 进行作进行建模的可行性，提供空间和垂直明确的预测。该概念验证建立了一种可扩展的方法，以弥合 WRF 建模中用于野火预测的关键差距。