BARA: cellular automata simulation of multidimensional smouldering in peat with horizontally varying moisture contents

Dwi M. J. Purnomo; Eirik G. Christensen; Nieves Fernandez-Anez; Guillermo Rein

doi:10.1071/WF23042

RESEARCH ARTICLE (Open Access)

Previous Contents Vol 33(2)

BARA: cellular automata simulation of multidimensional smouldering in peat with horizontally varying moisture contents

Dwi M. J. Purnomo ^A , Eirik G. Christensen ^A , Nieves Fernandez-Anez ^B and Guillermo Rein

^A ^*

+ Author Affiliations

- Author Affiliations

^A Leverhulme Centre for Wildfires, Environment and Society and Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, UK.

^B Department of Safety, Chemistry and Biomedical Laboratory Sciences, Western Norway University of Applied Sciences, Bjørsonsgate 45, 5528, Haugesund, Norway.

^* Correspondence to: g.rein@imperial.ac.uk

International Journal of Wildland Fire 33, WF23042 https://doi.org/10.1071/WF23042

Submitted: 27 March 2023 Accepted: 6 November 2023 Published: 23 January 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Australasian Society for the Study of Brain Impairment. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY)

Abstract

Background

Smouldering peatland wildfires can last for months and create a positive feedback for climate change. These flameless, slow-burning fires spread horizontally and vertically and are strongly influenced by peat moisture content. Most models neglect the non-uniform nature of peat moisture.

Aims

We conducted a computational study into the spread behaviour of smouldering peat with horizontally varying moisture contents.

Methods

We developed a discrete cellular automaton model called BARA, and calibrated it against laboratory experiments.

Key results

BARA demonstrated high accuracy in predicting fire spread under non-uniform moisture conditions, with >80% similarity between observed and predicted shapes, and captured complex phenomena. BARA simulated 1 h of peat smouldering in 3 min, showing its potential for field-scale modelling.

Conclusion

Our findings demonstrate: (i) the critical role of moisture distribution in determining smouldering behaviour; (ii) incorporating peat moisture distribution into BARA’s simple rules achieved reliable predictions of smouldering spread; (iii) given its high accuracy and low computational requirement, BARA can be upscaled to field applications.

Implications

BARA contributes to our understanding of peatland wildfires and their underlying drivers. BARA could form part of an early fire warning system for peatland.

Keywords: cellular automata, climate change, fire, hydrology, modelling, peat moisture, peatlands, wildfires.

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