EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modelling spatial and temporal patterns of fire due to human activity 

Oliver Perkins1, James Millington1, Sarah Matej2, and Karlheinz Erb2
Oliver Perkins et al.
  • 1King's College London, London, UK (
  • 2University of Natural Resources and Life Sciences, Vienna, Austria

Despite recent climate change producing more favourable conditions for landscape fire in many regions, studies of remote sensing data have suggested that global burned area is declining. The reasons for this are poorly understood but land use change, landscape fragmentation and CO2 fertilisation have all been suggested as contributing factors. Understanding human-fire interactions has been hampered by fragmentation of work across multiple disciplines – including geography, anthropology, land economics and ecology – and much case-study work in specific local locations. Consequently, coherent understanding of how contemporary anthropogenic land use and associated fire management strategies influence spatial and temporal patterns of fire globally has not yet been established.

To address this challenge, we have developed WHAM! – the Wildfire Human Agency Model - parameterised using the global empirical Database of Anthropogenic Fire Impacts (DAFI, [1]). This new model is driven by explicit representations of human behaviour, drawing on agent functional types to capture categorical differences in anthropogenic approaches to fire management globally. We present initial results and evaluate WHAM! using land management data based on the Human Appropriation of Net Primary Production (HANPP) and find good agreement between model outputs and these independent data. Further, to enable a like-for-like comparison with moderate resolution remote sensing products, we present a model emulator to screen model outputs of small agricultural fires (0.5-21 ha). 

We discuss how WHAM! shows land use intensification in South America, itself driven by increases in global demand for meat, has led to a substantial decline in anthropogenic fire use. This provides a partial process-based explanation of declines in global burned area observed from remote sensing. We discuss implications for understanding global spatio-temporal patterns of wildfire and share how fellow modellers can access model data and code. 

[1] Perkins and Millington (2021) DAFI: a global database of Anthropogenic Fire. Figshare. 

How to cite: Perkins, O., Millington, J., Matej, S., and Erb, K.: Modelling spatial and temporal patterns of fire due to human activity , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2462,, 2022.


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