This project’s aim is to devise a simplified, generic version of the fire-spread algorithm used operationally by the US and other fire services, and embedded in a number of global fire models. This research builds on two computational Masters projects previously supervised by Colin Prentice, and involves advanced engineering mathematics skills and familiarity with landscape-scale modelling.

Leadership Team

Much of the discussion of the drivers of recent changes in fire regimes has focused on the impact of climate change and anthropogenic activities. However, the direct impacts of changing atmospheric CO2 on plant growth and water-use efficiency could also affect biomass production and hence fuel loads, and therefore influence future fire regimes. The record of changes in fire regimes during intervals of low CO2 in the past provides an opportunity to separate the influence of climate and CO2 on fire regimes, and to test the predictions of fire-enabled dynamic vegetation models about this interplay. This project focuses on the analysis of fire regimes during rapid climate warmings during the last glacial epoch (Dansgaard-Oeschger events) and examines the consequence for biodiversity by comparing these with independent reconstructions of vegetation cover. The findings are applied to assess the likely impact of increasing CO2 for future fire regimes and biodiversity. The project involves analysis of existing palaeoenvironmental databases and fire modelling.

Leadership Team

Leadership Team

Leadership Team

This project will involve a global analysis of studies undertaken on local and traditional fire management and the development of a framework and methodology to integrate local level fire knowledge and information into larger scale models of fire dynamics. A key element of the approach will be to identify different forms of data derived from local level studies and to create procedures and rules that can inform the quantification and optimisation of quantitative, conceptual and decision support fire modelling. The postholder will work closely with a large number of academics, postdoctoral researchers and PhD students across the natural and social sciences in order to build the integrative methodology that will contribute to interdisciplinary scientific understanding of fire, its drivers, and its impacts.

Leadership Team

This core Palaeofire project will be central for the analysis of changes in fire regimes in response to past environmental and climate changes, using large-scale data synthesis and fire modelling. The PDRA leading it will be responsible for the collation and analysis of relevant palaeodata sets, including data on fire regimes, vegetation, peat growth, land-surface hydrology and climate reconstructions. This work will involve updating existing global data sets, through collaboration with international groups such as the PAGES Global Palaeofire Working Group or the PAGES C-peat project. It will involve creating and promoting new data compilations, for example for regional vegetation changes. In addition to analysis of the palaeodata, the PDRA will be involved in the design and analysis of model experiments to test explicit hypotheses about the response of fire to environmental and climate change in the past, including running specific palaeo-experiments using state-of-the-art fire-enabled vegetation models.

Leadership Team

This is an advanced data analysis project, requiring extensive hands-on experience with Earth Observation (EO) data from multiple platforms and well-developed IT skills. The project will deploy novel methods to jointly analyse different kinds of EO data (including fire attributes, vegetation and landscape properties, and indicators of settlement patterns) with the goal of developing a top-down global classification of fire regimes.

Leadership Team

The focus of this project is to interact with all project scientists in order to continuously develop and advance our capabilities in global wildfire modelling and its integration into Earth system models. The tasks will involve a) Algorithm development based on quantitative and qualitative insight from individual projects in different strands; b) Model evaluation and benchmarking; c) Integration into the UK Earth System Model (UKESM) and subsequent evaluation of performance of atmospheric composition, vegetation, and related systems. The PDRA leading this will need to have expertise in global Earth system modelling (e.g. focusing on the atmosphere, and/or dynamic vegetation) and experience with global model evaluation against a range of large datasets.

Leadership Team

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