Acquiring detailed 3D fuel data for advanced fire models remains challenging, particularly at large scales. To address this need, we present FastFuels, a novel platform designed to generate detailed 3D fuel data and accelerate the use of advanced fire models. FastFuels integrates existing fuel and spatial data with innovative modeling techniques to represent complex 3D fuel arrangements across landscapes. It leverages data sources including the Forest Inventory and Analysis (FIA) database and plot imputation maps, and incorporates advanced features such as data assimilation from LiDAR. This research demonstrates FastFuels’ capabilities through two applications: evaluating fuel treatment effectiveness with the Fire Dynamics Simulator and simulating a prescribed fire operation using QUIC-Fire. FastFuels provides previously unavailable 3D fuel data at landscape scales, empowering informed decision-making, detailed investigations of fuel treatment impacts, and higher-resolution risk assessments. Its flexible data assimilation and model-agnostic outputs accelerate advanced fire science and support fire management decisions.

中文翻译：

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FastFuels：利用高分辨率 3D 燃料数据和数据同化推进野火建模


为高级火灾模型获取详细的 3D 燃料数据仍然具有挑战性，尤其是在大比例尺下。为了满足这一需求，我们推出了 FastFuels，这是一个新颖的平台，旨在生成详细的 3D 燃料数据并加速高级火灾模型的使用。FastFuels 将现有的燃料和空间数据与创新的建模技术集成在一起，以表示跨景观的复杂 3D 燃料排列。它利用包括森林清查和分析 （FIA） 数据库和绘图插补图在内的数据源，并结合了 LiDAR 数据同化等高级功能。这项研究通过两个应用程序展示了 FastFuels 的能力：使用 Fire Dynamics Simulator 评估燃料处理效果，以及使用 QUIC-Fire 模拟规定的火灾操作。FastFuels 在景观尺度上提供以前无法获得的 3D 燃料数据，有助于做出明智的决策、详细调查燃料处理影响以及更高分辨率的风险评估。其灵活的数据同化和与模型无关的输出可加速先进的消防科学并支持消防管理决策。