Spaceborne LiDAR-derived height-variable forest wind reduction factors (WRF) for improved wildfire spread modelling

• Variability in landscape scale within-forest vertical Wind Reduction Factors (WRF) characterised across 11 diverse sites. • New low-parameter model proposed for predicting vertical and horizontal change in WRF for operational fire prediction. • WRF profile successfully predicted using GEDI derived vertical forest structure. • Proof-of-concept shows potential for GEDI-based WRF model to be applied globally for improved fire ROS prediction. 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.