FastFuels: Advancing Wildland Fire Modeling with High-Resolution 3D Fuel Data and Data Assimilation

Published

July 2, 2024

Author(s)

Anthony Marcozzi, Lucas Wells, Russel Parsons, Eric Mueller, Rodman Linn, Kevin Hiers

Abstract

Acquiring detailed 3D fuel data for advanced fire models remains challenging, particularly at large scales. This study presents 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 like 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 FDS simulations 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.

Citation

Environmental Modeling and Software

Pub Type

Journals

Keywords

Fuel modeling, Remote sensing, Wildfire modeling, Wildland fire, Prescribed fire

Citation

Marcozzi, A. , Wells, L. , Parsons, R. , Mueller, E. , Linn, R. and Hiers, K. (2024), FastFuels: Advancing Wildland Fire Modeling with High-Resolution 3D Fuel Data and Data Assimilation, Environmental Modeling and Software (Accessed October 19, 2024)