Project Description:
Feedbacks between fire, vegetation and climate are complex and vary by biome, but are also influenced and shaped by humans through land use and fire management activities. Recently, studies have found that observed declines in global burned area have been human-driven, but also that existing models of global fire are unable to reproduce the pattern and magnitude of these declines. Improving representation of human actions and decisions in dynamic global vegetation models (DGVMs) is an outstanding contemporary challenge for understanding the dynamics and possible future states of the earth system. Although primarily used to-date at smaller extents, agent-based modelling (ABM) approaches have been proposed for improving representation of human activity at global extents in DGVMs. Much of the focus of this work on scaling-up ABM has focused on land use change driven by exploitation of natural resources, neglecting the use and suppression of fire.
This PhD project will take recent ideas about generalised representation of human activity and behaviour to implement an ABM of anthropogenic fire for coupling with DGVMs. This ABM will need to represent the variety of ways in which people currently use and interact with fire, including for example land use, land use transitions, land tenure and capitalisation, seasonality of burning, and the distinction between local actions (e.g. individual farmers, land owners) and broader institutional influences (e.g. state-led fire suppression policies). The project will build on existing model frameworks (e.g. CRAFTY, LPJ-GUESS) and enable large-scale examination of the complexity of human-environment interactions in the Anthropocene.
Policy Impact of Research:
Understanding anthropogenic fire in the earth system is vital to identify policy and management options to mitigate biomass burning emissions and air quality. By improving modelling of anthropogenic fire this project will contribute to these policy and management challenges.
