An improved approach for simulating peat ignition probability using experimental data

Peatlands, despite covering only 3% of the terrestrial surface, are one of the world's most important carbon storage environments, accumulating around 25% of the total soil carbon. However, climate change is increasing the vulnerability of these carbon-rich ecosystems to fire, with potentially severe implications for the global climate. Warmer and drier conditions, driven by climate change, are expected to intensify and increase the frequency of peat fires, potentially transforming peatlands from carbon sinks into net sources of greenhouse gas emissions. Such a shift could trigger a positive feedback loop, accelerating climate change through the release of vast amounts of sequestered carbon into the atmosphere.

While incorporating peatland fire feedbacks into Earth System Models (ESMs) is essential for accurate climate projections, the majority of the existing models lack a representation of peat fires, limiting their ability to predict future climate dynamics effectively. Understanding the smouldering behaviour of organic soils, their ignition probability, and how these processes can be represented at a global scale is essential. The current state-of-the-art approach to compute peat combustibility, established by Frandsen (1997) and applied in recent peat fire modelling efforts (e.g., INFERNO-peat), relies on a parameterization derived from a single peat type, hampering its global applicability. Frandsen (1997), by conducting experiments on natural peat samples developed an empirical model for smouldering ignition probability based on three key properties of peat: moisture content, inorganic content, and bulk density.

Our study proposes an improved method for calculating peat combustibility by optimizing the coefficients in Frandsen’s model and investigating the ignition limits of diverse peat samples. The optimization process utilized experimental data from seven distinct peat types. First, we established through inverse modelling a link between inorganic content, bulk density and critical moisture content, the moisture threshold above which smouldering cannot be self-sustained. Then we determined the probability distribution of self-sustained smouldering, as a function of moisture content, around the critical moisture content, also employing inverse modelling. The combination of both optimizations yielded consistent coefficients, providing a more robust framework for modelling peat ignition probability.

By improving the representation of peat ignition probability using experimental data from both previous studies and our own experiments, this work aims to upgrade the simulation of peat fires in fire models and ESMs, enhancing our understanding of the impacts of such fires on future atmospheric composition, radiative forcing, and climate.