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

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.