A model of wind speed reduction (WRF) in forests that can be parameterised globally using spaceborne LiDAR

Sub-canopy windspeed is a critical input variable in wildfire simulation modelling because it has a strong effect on the predicted rate of fire spread (ROS). In vegetated landscapes, windspeed reduction occurs due to the structural properties of vegetation, with the canopy height and forest/vegetation 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 estimate WRFs at different heights within the forest profile and across landscapes. We utilise a spatiotemporally unique dataset of twenty-six sub-canopy WRFs measured at 2m height and derived from approximately five years of within-forest windspeed data across a range of vegetation types with diverse structural attributes. Model validation was conducted using observed (measured) vertical WRF profiles across 12 structurally diverse sites. The observed within-canopy WRF across the height range (1 – 75m) varied from 2.3 to 16.1. The new WRF model achieved a Kling-Gupta Efficiency (KGE) score of 0.8 and a coefficient of determination of 0.73, indicating very good agreement between the modelled predictions and the observed WRF data. The Mean Absolute Error (MAE) of the model was 1.36, and there was a slight bias towards overprediction of 0.43. The model represents an advancement in operational WRF modelling by explicitly integrating large-scale spatial datasets that characterise vertical forest structure. It demonstrates the feasibility of using GEDI data to model WRFs operationally, providing spatially and vertically explicit predictions. As a globally available dataset, GEDI enables this approach to be applied in forests/vegetation worldwide to better represent variability in WRF and therefore improve fire ROS modelling. This proof-of-concept establishes a scalable method to bridge critical gaps in WRF modelling for wildfire prediction.