Knowledge Bank

Wildfires abruptly reduce vegetation cover and gross primary productivity (GPP), a measure of photosynthesis, with the complete recovery process extending beyond a decade. Post-fire recovery of vegetation in mountainous ecosystems is particularly complex due to topography’s influence on local hydrology and climate. Most existing studies on post-fire vegetation recovery primarily focus on the short-term response while neglecting long-term legacy effects and use vegetation metrics other than GPP. Here, we address these gaps by using long-term records of remotely sensed burn area and GPP from the Landsat Burned Area Product and MODIS GPP in a space-for-time substitution approach to analyze the spatial-temporal patterns of recovery over multi-decadal periods in North-Central Colorado. We further examine how climate, environmental, and fire-related conditions contribute to the observed variation in recovery by running 17 such predictors in a Random Forest machine learning regression model. We find that GPP in burned areas within the study area recovers more slowly under conditions that are hotter, more arid, and that burned under higher severity fires, with maximum temperature anomalies being the most important predictor of post-fire GPP recovery. As post-fire climatic conditions are identified as the most important predictors of GPP recovery, these findings suggest that increasing temperatures under climate change are expected to inhibit post-fire GPP recovery, especially compounded with drought and severe burns, pointing to the value in considering post-fire climate to evaluate the impact of wildfires on terrestrial carbon budgets.