Unraveling Post-fire Forest Recovery in Russia: Spatial Heterogeneity and Climatic Constraints

Wildfires are intensifying across boreal regions under climate warming, causing biomass losses, yet post-fire forest recovery and its drivers remain poorly quantified at large spatial scales in Russia. Here, we delineated forest fire disturbances by integrating multiple satellite-derived burned-area datasets with a consensus-based approach to reduce dataset uncertainties. We then applied a space-for-time substitution framework integrating forest fire disturbance, forest age, and aboveground biomass to quantify key forest recovery metrics for forest biomass recovery: AGBmax (potential maximum aboveground biomass), R30 (recovery rate at 0-30 yr), and T90 (recovery time to reach 90% of AGBmax). These metrics were validated against field measurements, and their environmental drivers were further explored using machine learning models. Forest fire hotspots were concentrated in central Russia, 151.3 Mha of cumulative burned area during 1985-2022. The median values of AGBmax, R30, and T90 were 99.7 Mg ha^-1, 1.83 Mg ha^-1 yr^-1, and 127 yr, respectively. Spatial patterns were highly heterogeneous, with southern and western regions showing higher AGBmax and faster R30, while eastern and southwestern regions exhibited longer T90. Validation against field observations confirmed that the fitted curves closely reproduced observed recovery trajectories. Climate conditions, especially drought and low solar radiation, strongly constrain R30 and AGBmax, and while also prolonging T90. These findings enhance our understanding of post-fire forest resilience and the climatic controls on recovery across Russia, providing a robust foundation for future assessments of ecosystem carbon dynamics.