Primary Supervisor – Dr Matthew Jones
Secondary Supervisor – Dr Rachel Carmenta
Supervisory Team – Prof Crystal Kolden (University of California), Prof Rachel Warren
Prescribed burning is a prominent practice used by land managers, including Indigenous peoples, in many fire-prone forests to mitigate wildfires, and in savannah-grasslands to remove invasive species or rejuvenate vegetation for grazing.
Prescribed burning is only feasible during a seasonal ‘window’ of opportunity with moderately wet and cool weather conditions, however in some regions4 the window of opportunity is shifting or shortening due to climate change. During the coming decade, it is critical that land managers prepare for climate-driven changes in opportunity to practice prescribed burning, e.g. by increasing personnel numbers to build capacity to conduct burns in a shorter window.
The PhD student will study the past and future effects of climate change on prescribed burn windows at the global scale for the first time.
They will combine a new dataset of contemporary prescribed burns from 28 countries with meteorological data to determine the climatic characteristics of prescribed burn windows (during 1990-2021), and then use climate models to predict the future impacts of climate change on these windows through 2100.
Moreover, they will combine qualitative interviews5 with Q-method to understand the perceptions of land managers as to the challenges/implications that climate change poses for their prescribed burn programmes. Climate model predictions will be shared with land managers, and qualitative interviews will be repeated to assess how the new quantitative information changes perceptions of managers.
- Identify the climatic thresholds of contemporary prescribed burn windows, by combining regional records from partnering land managers with meteorological observations.
- Predict future changes in the duration/timing of the window using climate models.
- Conduct semi-structured interviews with land managers and use Q-method to assess perceptions of prescribed burning in the context of historical and future climate change, and in light of model predictions.
- Geospatial analysis of meteorological data/climate model outputs in Python/R.
- Social science methods including Q-method5.
- Extended visit to UC Merced and interaction with a prescribed burn programme.
- International conferences
- Tyndall Centre membership.
- Minimum 2:1 in any natural science or data science.
- Desirable: Geospatial analysis using code (Python/R).