Wildfire disturbance-recovery dynamics in Southern Siberia: feedbacks between climate change and ecosystem resilience

This project will result in methods to detect boreal recruitment failure (RF) due to fire, an explanatory model of RF, and quantification of climate feedbacks from RF that are not currently accounted for in any climate or vegetation model. The associated data collection and research outputs will benefit models of climate-fire-vegetation feedbacks. Presently all models that incorporate fire disturbance assume forest recovery. Research Questions 1. When and where does boreal RF occur? 2. What are the factors that cause boreal RF? 3. What climate feedbacks are likely to result from boreal RF? Forest loss due to the failure of new trees to survive (recruitment failure) post-fire occurs in boreal forests in Eurasia and North America. The existence of ecological thresholds, or "tipping points" that cause abrupt ecological shifts, is well-known in ecosystems theory but where and when ecosystems are approaching such dramatic changes is difficult to predict. One such extreme ecological shift has been observed in boreal forests that fail to recover after multiple fires within a short time interval (< 10 years). These areas are dominated by grass and are similar to steppe vegetation. Transition from forest to steppe is consistent with predicted changes in vegetation composition in response to regional climate change, and is consistent with global observations of forest loss in response to climate. Preliminary analyses of these sites indicate causes related to changing fire regimes effected by climate. Firstly, although vegetation indices have been used to identify forest loss, there is currently no method to detect RF using remotely sensed data. We address here the likelihood that RF produces a unique signature that can be detected remotely. The total area affected by RF in Eurasia and North America is at present unknown. Using RF locations provided by the Sukachev Institute (see letter of support), we have developed preliminary methods to differentiate between successfully recovery from fire and RF using remotely-sensed vegetation indices. The proposed research would refine these methods and develop an automated approach to detect RF. The lengthening satellite data record permits a new focus on the impact of climate change on boreal forests (the largest terrestrial biome) and its potential consequences. Remotely sensed imagery to date have yielded "snapshots" of ecosystems and disturbance events. With more than a decade of daily imagery from the MODIS sensors, we can begin to monitor processes like disturbance-recovery cycles. This new focus is critically important to the study of climate-ecosystem interactions and climatic "tipping points". Secondly, the causes of RF have not been identified. RF has been observed in areas of Siberia where the length of time between fire disturbances was extremely low. Initial field observations of RF sites indicate that high soil temperature and low moisture create a seedbed unsuitable for recruitment of trees following a fire. Additional field data will provide the inputs for an explanatory model of RF that includes characteristics of the fire (such as intensity and fire weather), pre-fire vegetation (e.g., stand age and density), and post-fire environment (e.g., soil temperature and moisture). Thirdly, the effect of RF on carbon, water and energy fluxes that impact climate has not been quantified. The replacement of forests with steppe vegetation results in carbon losses to the atmosphere from combustion and post-fire decomposition. The net climate impact of RF is presently unknown. Albedo is initially low following a fire and then may become higher due to the higher albedo of replacement vegetation. Changes in evapotranspiration rates affect latent and sensible heat fluxes. The area of RF is likely to grow in response to increasing fire frequency and severity, but the dynamics of recovery from wildfire and RF have not been incorporated into any coupled climate-vegetation models.