FastFuels: Advancing wildland fire modeling with high-resolution 3D fuel data and data assimilation

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. • FastFuels generates detailed 3D fine fuels data across large areas by integrating FIA data and advanced imputation methods. • The platform simulates various silvicultural treatments and can facilitate assessment of their impact on fire behavior and effects. • FastFuels employs a novel nested modeling approach to convert individual tree descriptions into 3D voxel models suitable for advanced fire simulations. • Ground and aerial measurements, including LiDAR and UAS data, can be integrated to continuously refine fuel representations. • The platform generates model-agnostic fuel inputs, facilitating model evaluation and inter-model comparisons.