Impact of Meteorological Conditions on Post-fire Recovery of Boreal Forests across Canada

Over the past years, boreal forests of Canada have been increasingly affected by large and high-severity wildfires, with recent fire seasons recording unprecedented burned areas across the country. Alongside these extreme wildfires, the ecosystems have been forced to recover under frequent climate extreme events, including prolonged droughts and intense heatwaves, which have often occurred compounded. In this study, we propose a preliminary framework to analyse the association between meteorological conditions and their impact on post-fire recovery over three major eco-regions of Canada - Western Canada, the Great Plains, and Eastern Canada. Considering the period 2001-2025, we first estimate the post-fire vegetation recovery rates using a mono-parametric model based on the remotely-sensed Enhanced Vegetation Index (EVI). Then, we apply a Random Forest (RF) modelling approach that integrates SHAPely Additive exPlanations (SHAP), aiming to explain how seasonal meteorological variables, which include air temperature, precipitation, snow depth, and solar radiation, influence the forest recovery process.

Among the three eco-regions, the recovery model exhibits a consistently strong performance. Forests in Western Canada generally show faster post-fire recovery, contrasting with slower recovery rates observed in the Great Plains, although considerable intra-regional contrasts are found. The RF models and the associated SHAP-based results effectively identify key meteorological drivers of burned forest recovery, showing an overall good performance across the three regions. The model tends to give higher importance to variables that strongly control the growing season in boreal ecosystems, namely solar radiation and air temperature during transitional seasons, particularly in spring. In Western Canada, solar radiation and air temperature roughly constitute the most influential features on recovery, whereas in the Great Plains and Eastern Canada, autumn precipitation emerges as the primary controlling feature. Additionally, both precipitation and air temperature extremes in winter and summer frequently appear as secondary drivers of recovery rate, highlighting that climate extreme events may display an important modulating effect on post-fire recovery.

Our preliminary framework provides a novel approach to estimate the recovery rate of burned vegetation across Canada based on a time-series analysis, rather than space-for-time substitution methods. Furthermore, the application of machine-learning techniques combined with SHAP provides new insights related to seasonal and regional roles of meteorological variables in modulating post-fire vegetation recovery processes.

This work was performed under the framework of DHEFEUS project, funded by Portuguese Fundação para a Ciência e a Tecnologia (FCT) (https://doi.org/10.54499/2022.09185.PTDC).