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Upcoming Events

11:00 am (CT)


Jiafu Mao

Senior Staff Scientist

Oak Ridge National Laboratory

Machine-Learning Applications in Process-understanding and Prediction of Wildfire

Wildfires are a major land disturbance and aerosol emission source, affecting the global carbon budget, climate, and socioeconomic development. However, the driving mechanisms underlying fire evolution and reliable prediction of fire activity remain to be explored, especially in the fire prone regions. Here, I will present our recent studies aimed at investigating the wildfire drivers and predictability using machine learning techniques (MLTs), satellite observations and Earth system model (ESM) simulations. We quantified the natural and anthropogenic controlling factors underlying global fire changes for the period 2003–2019 and highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area. We assessed the seasonal environmental drivers and predictability of African fire and achieved skillful prediction of African fire one month in advance. Moreover, we constrained fire carbon emissions simulated by the latest ESMs during the twenty-first century and refined the regional wildfire exposure in different socioeconomic factors. Overall, our research confirmed the feasibility and efficiency of ensemble MLTs in wildfire attribution, modeling and prediction.

11:00 am (CT)


Marie-Josee Fortin

University Professor

Department of Ecology and Evolutionary Biology, University of Toronto

Combining network theory and tree functional traits to improve forest resilience to global change

To manage forests as complex adaptive systems, approaches based on resilience, functional diversity, assisted migration, and multi-species plantations can be used. We propose a novel approach to integrate the functionality of species-traits into a functional complex network approach as a flexible and multi-scale way to manage forests for the Anthropocene. This approach takes into consideration the high level of uncertainty associated with future environmental and societal changes. It relies on the quantification and dynamic monitoring of functional diversity and complex network indices to manage forests as a functional complex network. Using this novel approach, the most efficient forest management and silvicultural practices can be determined, as well as at what scale and at what intensity landscape-scale resistance, resilience, and adaptive capacity of forests to global changes can be improved.

Joint RSS Environmental Stats Section (ESS) and TIES speaker


Hayley Fowler, Newcastle University