Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

For peat’s sake! Peat type influences critical moisture thresholds that prevent combustion of organic soils in Western Australia

Valerie S. Densmore https://orcid.org/0000-0003-0121-8709 A C * and Taiya K. Barnesby B
+ Author Affiliations
- Author Affiliations

A Biodiversity and Conservation Science, DBCA, Manjimup, WA 6258, Australia.

B Parks and Wildlife Service, DBCA, Manjimup, WA 6258, Australia.

C Present address: Wilinggin Aboriginal Corporation, Derby, WA 6728, Australia.

* Correspondence to: dwc@wilinggin.com.au

International Journal of Wildland Fire 34, WF24204 https://doi.org/10.1071/WF24204
Submitted: 28 November 2024  Accepted: 8 August 2025  Published: 5 September 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Preventing ignition of peatlands presents a particular challenge in Western Australia due to a decreasing trend in annual rainfall over the past several decades.

Aims

We sought to identify critical moisture thresholds and other factors, including chemical composition, geomorphology or peat type that influence the potential for peatlands to sustain smouldering combustion.

Methods

We wet soil turves from 16 distinct seasonally waterlogged peatlands to pre-determined moisture contents before exposing samples to a heating element to induce smouldering, and then calculated weight and volume loss due to combustion. Other turve portions were used to conduct physical and chemical analyses.

Key results

Critical moisture thresholds for ignition and combustion varied by peat type due to differences in bulk density and carbon content. Models predicting combustion that contained the explanatory variables peat type, electrical conductivity (EC) of soil and moisture content achieved R-squared values above 0.8.

Conclusions

Our results indicate the moisture thresholds to prevent ignition of peatlands differ between peat types; knowledge that is important to inform effective decisions made by fire managers during planned fire and bushfire operations.

Implications

Determining critical moisture thresholds and peat properties that influence peatland flammability informs potential mitigation techniques to reduce the incidence of smouldering peatlands.

Keywords: flammability, ground fire, peatlands, peat type, planned burning, smouldering combustion, soil moisture, temperate ecosystems.

Introduction

Peat fires pose considerable hazards to firefighters, biodiversity, greenhouse gas concentrations and public health, and thus constitute a serious national and international issue. Peatlands combine flammable vegetation over organic substrates that may extend several centimetres to metres deep in a low-oxygen environment, representing a sizeable pool of stored carbon (e.g. Page et al. 2002; Grishin et al. 2009; Beaulne et al. 2021). When these organic-rich soils ignite, smouldering ground fires produce toxic emissions and promote char formation, high temperatures and a propensity for flare-ups and reignition of above-ground fuels over many months (Jones 2005; Huang and Rein 2014; Hu et al. 2018). Australian peatlands are also ecologically susceptible to fire due to their restricted extent, long recovery times and provision of specialised habitat to migratory or endemic species (e.g. Horwitz 1997; Pemberton 2005; Whinam and Hope 2005; Benson and Baird 2012; Winkle et al. 2021). It is unequivocal to avoid peatlands catching alight for these reasons, but avoiding peat fires is difficult to achieve.

Organic soils can become flammable when dry, with the degree of flammability dependent on the composition and the moisture content of the soils (Frandsen 1987; Semeniuk and Semeniuk 2005; Reardon et al. 2007; Huang and Rein 2015; Prior et al. 2020). Moisture thresholds for peat ignition are known to vary as a function of bulk density, organic carbon and inorganic content (e.g. Frandsen 1997; Reardon et al. 2007; Benscoter et al. 2011; Huang and Rein 2015; Huang et al. 2015; Prat-Guitart et al. 2016; Prior et al. 2020). Moisture thresholds around 110% have been shown to prevent ignition of disaggregated peat (e.g. nursery peat) compressed to a bulk density akin to peat turves (Frandsen 1987; Huang and Rein 2015; Prat-Guitart et al. 2016; Huang and Rein 2017). Higher moisture thresholds were necessary to prevent ignition of peat turves cut retaining much of their original structure, cut from peatlands in North America (120%, Frandsen 1997) and the United Kingdom (115–135%, Rein et al. 2008). This difference between compressed disaggregated peat and peat turves is likely due to differences in bulk density or other structural factors. Organic carbon content varies between peatlands, affecting moisture-holding properties, emissions and combustion dynamics measured as different mass and volume loss (Hu et al. 2018). Tropical peats typically have 12% more organic carbon content than temperate peats, but organic carbon content also varies between more localised peatlands. Inorganic content also varies between peatlands according to their geoformation and other processes (e.g. Semeniuk and Semeniuk 2005) and has an inverse effect on moisture thresholds to prevent peat ignition. For example, peat samples collected across the United States (Frandsen 1997) and Tasmania, Australia (Prior et al. 2020) showed lower moisture thresholds when the peatland soils had higher inorganic content. These natural variations of bulk density, organic and inorganic content across peatlands makes it unclear whether a single moisture threshold is sufficient to prevent ignition of all peats.

The hydrology of peat systems can also affect the rate of peat formation, nutrient status, inorganic content and physical structure (i.e. bulk density) (Gore 1983; Moore 1987). In a Floridian cypress bog study, soil moisture content was found to vary according to a sample’s distance to the edge of a peatland, and soil moisture alone did not predict depth of peat burning (Watts 2013). Studies from temperate peatlands in North America and the United Kingdom have shown botanical composition of peat affects bulk density, combustion rate and the heat released during combustion that can affect moisture thresholds (Frandsen 1987; Grishin et al. 2009). Higher lignin contents can reduce the rate of decomposition and peat compaction, affecting relative bulk density (Collinson and Scott 1987). A recent study using structurally-intact soil turves from Tasmanian peatlands found the moisture required to prevent ignition ranged from 25 to 95% gravimetric moisture, depending on organic content and bulk density of the sample, which related to vegetation types ranging from sphagnum mosses to woody scrub (Prior et al. 2020). Most peat formations in southwest Western Australia (SWWA) originate from sedges and restionaceous species rather than sphagnum mosses, thus moisture contents needed in SWWA peatlands may vary from boreal and some Tasmanian Sphagnum moss peatlands (SWWA; Pemberton 2005; Whinam and Hope 2005).

Peats are commonly associated with wetlands, which have been classified in southwestern Australia using a combined hydroperiod and geomorphic approach (Semeniuk and Semeniuk 1995). Hydroperiod refers to the duration and timing of waterlogging or inundation, which may be permanent or seasonal. Geomorphologies include basins, flats, channels, slopes and highlands. Seasonally waterlogged basins, slopes, flats and highlands may contain peat-forming sedge species including Empodisma gracilliumum and Reedia spathacea (Winkle et al. 2021). Slow-moving smouldering fires that burn the organic substrate within these communities can cause substantial damage to root zones, which hinders or even prevents recovery of peatlands (Rein et al. 2008; Horwitz et al. 2009; Watts 2013; Turetsky et al. 2015; McDougall et al. 2023). Ecological effects of peat fires are exacerbated by the Mediterranean climate in SWWA that includes dry summer conditions not suited to re-establishing wet-adapted species.

The moisture content of peatlands varies seasonally with fluctuations in rainfall (Prior et al. 2020). Annual rainfall has demonstrated a decreasing trend over the past several decades in SWWA, leading to reduced recharge of groundwater systems important to sustain wetlands (Pemberton 2005; Semeniuk and Semeniuk 2005; Department of Environment and Conservation 2012; IPCC 2021). Recent summer bushfires have led to some deep peatland systems burning away to mineral earth, indicative of unprecedented drying. Fire managers advocate for the exclusion of fire from wetlands when possible, but the lack of sufficient recharge due to climate change increasingly puts peatlands at risk from prescribed burning operations. Recently, prescribed burning operations have ignited some deep peatland systems while leaving others nearby intact. These experiences have highlighted the need to identify critical moisture thresholds that influence the potential for peatlands to sustain smouldering combustion.

Given the mixed susceptibility of neighbouring peatlands to burn when exposed to the same fire, it is unclear if a single threshold is sufficient for all peatlands, or if moisture thresholds to prevent sustained combustion vary between peat types. The aim of this study was to determine (1) if peatlands within different geomorphologies exhibited different critical moisture thresholds, and (2) if peat classification alone would provide a sufficient key to determine operational moisture thresholds or additional factors were necessary to predict susceptibility to ignition. A further aim was to determine the mathematical relationship between gravimetric moisture and a soil moisture probe likely to be used in an operational setting.

Methods

Study region and sampling design

We compared moisture thresholds to prevent sustained combustion between peatlands from four different geomorphologies located within the Warren bioregion in SWWA, a Mediterranean climate region (Rundel et al. 2016) (Fig. 1). Gravimetric moisture content was used throughout this study. Wetland geomorphologies containing peatlands in SWWA include seasonally waterlogged basins (dampland), slopes (paluslope), highlands (palusmont) and flats (palusplain) (Semeniuk and Semeniuk 1997). Historic annual rainfall averages 1400 mm within the Warren bioregion, primarily occurring within the six consecutive wettest months (May–October). Minimum rainfall coincides with the three summer months when maximum temperatures often exceed 35°C and frontal systems bring dry lightning storms (Dowdy and Kuleshov 2014; Bates et al. 2018). Prescribed burning is regularly used to reduce fire hazard and promote conservation within SWWA, including the Warren bioregion (Burrows and McCaw 2013; Howard et al. 2020). A decreasing trend in rainfall in SWWA has been ongoing since the 1970s (IPCC 2021), contributing to less recharge of wetlands, inclusive of peatlands.

Fig. 1.

Map indicating the location of 16 peat sampling sites. Sites included four representatives from four seasonally waterlogged geomorphologies. Damplands (basin) shown by circles, Palusplains (flat) shown by squares, Palusmonts (highlands) shown by diamonds and Paluslopes (slope) shown by triangles. Red square in map inset indicates general location of sampling area within Western Australia.


WF24204_F1.gif

Field approach

We collected soil turves under an approved disturbance permit from 16 sites across the southwest of Western Australia spanning four representatives each from damplands, paluslopes, palusmonts and palusplains (Fig. 1). General site locations were selected using geographic information system (GIS) maps (ArcMap v10, ESRI, Redmond, WA, USA CA), and specific areas were chosen onsite to represent four sub-sites containing organic soil of sufficient depth and horizontal length. At each of the four areas, soil samples (20 cm wide × 120 cm long × 10 cm deep) were initially cut using a jab saw before subdividing into six individual turves and lifting away from the substrate using a trenching shovel. This provided 24 turves for each of the 16 sites. After a set of the six turves was collected, soil moistures were tested at five adjacent locations using an Acclima SDI 12 probe (Acclima, Meridian, ID, USA) to establish an average moisture content for each site. The soil turves were placed in a labelled, sealed plastic container and transported to the laboratory for further analysis.

Sample preparation

Soil turves were trimmed in the laboratory into 10 cm × 10 cm × 5 cm blocks and placed into labelled, pre-weighed aluminium foil trays. Samples were weighed, dehydrated by heating to 105°C for 48–72 h and reweighed to ascertain field moisture. A portion of the remaining turves were cut using a 393 cm3 steel tube sharpened on one end, weighed and dried and reweighed once dehydrated to calculate dry soil bulk density. A secondary portion was set aside for soil chemical testing by ChemCentre (www.chemcentre.wa.gov.au).

The Australian Soil Classification guidelines indicate peats can be divided into three types: fibric, hemic and sapric based on how decomposed the component plant materials are within the soil. We gauged decomposition first using visual assessments to determine if plant remains were distinct and identifiable (fibric), recognisable but difficult to identify (hemic), or indistinct to unrecognisable (sapric) (Isbell and National Committee on Soil and Terrain 2024 “Peat” in ASC-Glossary 1993). Subsequent tests involved squeezing wet peat samples to examine the colour of the liquid and how much peat was extruded between the fingers. The more decomposed the organic material, the more likely that water would be darkly coloured (hemic) or contain a high percentage of soil (sapric).

Six target gravimetric moisture contents were 200, 150, 100, 80, 60 and 0% w/v, and these were replicated four times for each of the 16 sites using sub-site turves. Foil trays containing the samples were placed in an open-ended vacuum-sealer bag before distilled water that was calculated to achieve the pre-selected moisture content was poured into the tray. The open end of the vacuum sealer bag was then sealed using a FoodSaver VS4500. Sealed samples were placed back in an oven at 80°C for 5–14 days as needed to facilitate absorption of water by the hydrophobic peat while preventing the vacuum bags from melting. After samples were observed to have taken up all of the water or had been heated continuously for 14 days, they were removed from the oven but left within sealed bags until just prior to the combustion procedure.

Combustion

The protocol used by Prior et al. (2020) and others (see Rein et al. 2008) was modified slightly to combust peat samples and provide a conservative estimate of critical moisture thresholds for sustained combustion. Ten separate combustion chambers were assembled using clay bricks on a concrete paver within a fume cupboard to fully enclose samples on four sides, and the bottom with a heat-resistant barrier (Fig. 2). Peat samples were removed from vacuum sealer bags, weighed in the foil trays and then placed within an individual chamber, before foil trays were re-weighed to account for any remaining moisture or peat not retained in the sample block. These weights were used to assess mass loss due to combustion and to calculate actual pre-burn moisture content compared to target moisture. A ruler was used to vertically measure each corner of the sample to the top of the adjacent brick.

Fig. 2.

Combustion chambers for peat samples showing top-down (a) and lateral (b) view. Note lateral view has had the end brick removed following a combustion stimulus. Clay bricks were used on all four sides and samples sat on a concrete paver within a fume cupboard. Photographs taken by V. Densmore.


WF24204_F2.gif

A flat heating element (350 mm long, 4.2 mm wide, 2.2 mm deep; 240 V, 200–250 W, J type inbuilt thermocouple formed to M shape; Yueqing Langchen Electrical Co, Hongqiao Town, China) was inserted between the brick and one side of the sample. A percentage voltage controller (Megawatt Heating Elements, Somerville, Victoria) was used to supply 100 W of power to the heating coil for 30 min, after which time the coil was switched off and gently extracted to minimise disturbance to samples, which remained in situ overnight to allow any smouldering combustion to complete, further evidenced by a lack of smoke or heat associated with the samples.

Once smouldering of the organic matter ceased, the vertical height from the top of the brick to the top of each of the four corners of the sample was remeasured. Samples were removed from the combustion chamber into pre-weighed foil trays, weighed and oven dried at 105°C for another 24–48 h to distinguish mass loss due to combustion rather than evaporation.

Physical and chemical analyses

In addition to the analysis of how gravimetric moisture content of organic soils influences smouldering combustion, pH levels, bulk density and chemical analysis was also conducted on samples from each of the 16 sites. This information further allowed us to determine how and why organic material combusts and smoulders the way it does in our natural environments.

A 1:5 soil:deionised water suspension was used to determine soil pH on four samples from each site (PH220S, Lutron, Taiwan) as per Rayment and Higginson (1992). Pre-measured steel corers were used to assess bulk density on four samples per site. Organic content was calculated as the maximum percentage of mass loss due to combustion. Additional tests conducted externally by ChemCentre included electrical conductivity (EC) of 1:5 soil extract, total carbon, total nitrogen, acid neutralisation capacity and soil particle distribution using laser diffraction (ISO 13320 – 1:2020).

Combustion strength

Volume loss was used to determine the ability of samples to sustain combustion while the heating element was applied or until most or all the sample was burned. For each sample, the number of corners where height decreased more than 1 cm or burned to the underlying paver, typically more than a 4 cm decrease, was tabulated and used to group samples into combustion strength classes, as per Table 1. A classification of moderate or strong was considered to represent potential for ongoing combustion of peat substrates in field situations.

Table 1.Criteria used to classify samples into combustion classes.

Combustion classDescriptionn includedn outliers
StrongThree or more corners have volume loss >4 cm340
ModerateTwo corners have volume loss >4 cm3928
WeakOne or more corners have volume loss ≥1 cm37107
NilNo corners have volume loss ≥1 cm24100

Sites that failed to show moderate or strong combustion in oven-dry samples were considered outliers for the purpose of this study and excluded from further statistical analyses. Two columns on the right denote total number of samples (n) found in each class that were included in statistical analyses or excluded as belonging to outlier sites. Note, samples were 5 cm tall (maximum volume loss 5 cm).

Some sites did not achieve moderate or strong classification even when samples from those sites were oven-dried. During field sampling, some sites appeared to have been altered by previous fire events, evidenced either by exposed roots or soil pedestals beneath charred shrubs, generally depicting a loss of 30 cm or more wetland substrate. The sites exhibiting these features were not routinely recorded, but the lack of combustion when samples were oven-dried supported the hypothesis that the peat at some locations had already combusted. Therefore, sites that failed to show any moderate or strong combustion when samples were oven-dried were considered to be outliers for the purposes of this study (Table 1). These outliers were removed from the dataset prior to statistical analysis.

Statistical analyses

Logistic regression was used to compare how varying gravimetric moisture contents would affect probabilities of smouldering combustion among different geomorphologies and peat types. Volume loss was converted to a binary variable where a moderate or strong classification as per Table 1 was considered successful combustion. Logistic regression was performed using the glm() function, for generalised linear model (GLM), that was modified to compute logistic regression by designating the family arguments binomial in the native stats package within R (R Core Team 2024). Separate models were run for different geomorphologies and by peat type, and fitted values from the models were graphed to facilitate comparisons.

GLMs were used to compare gravimetric moisture contents and percentage mass loss associated with each combustion class across geomorphologies and peat types. Distribution histograms indicated the data was right skewed, so a Gamma family and inverse link was used with the glm() argument within the native stats package. Where appropriate, estimated marginal means were compared using the emmeans package (Lenth 2025) with the tukey method to adjust P-values for comparing a family of three estimates.

Two-way analysis of variance (ANOVA) was used to test whether peat type or geomorphology interacted with gravimetric soil moisture content to affect mass or volume loss of peat samples. Percentage rather than absolute mass loss and volume loss was used. Samples were collected considering geomorphology, but peat type could only be assessed as an edaphic factor after samples were collected. Analysing peat type as a categorical edaphic factor produced an unbalanced design that was addressed by using type III sums of squares.

Proportional odds regression generalised additive models (GAMs) were used to model the abilities of edaphic factors to predict combustion class due to its ordinal characteristics. The GAMs were run using with the mgcv package (Wood 2011) in R. Due to small sample sizes, the Bayesian Information Criterion (BIC) was used to detect the most parsimonius model and Nagelkereke r-squared (fmsb package; Nakazawa 2024) to determine predictive power. Partial dependence plots were produced for variables in the best performing models using the pdp (Greenwell 2017) and ggplot2 (Wickham 2016) packages in R.

All statistics were conducted using R version 4.4.0, ‘Puppy Cup’ (R Core Team 2024).

Results

The gravimetric moisture content needed to prevent successful combustion of peat samples varied significantly among both geomorphologies (P = 0.033, F = 3.516) and peat types (P = 0.044, F = 3.249) (Table 2, Fig. 3). Binomial models predicted 63.1% gravimetric moisture would prevent sustained combustion for paluslopes, while basins required 80.6% moisture and palusplains would need 101% moisture (i.e. g H2O/100 g dry soil). Palusplains also showed the highest measured gravimetric moisture to prevent ignition (131.3%), compared to 100.6% for basins and 77.7% for paluslopes (Table 2). Geomorphologies did not help define a clear pattern of mass loss across combustion classes (Table 3, Fig. 3c).

Table 2.Gravimetric moisture thresholds measured for each combustion class across geomorphologies and peat types.

GeomorphologyUnburnedStrongModerateWeakNil
Basin80.6 ± 7.4A,B11.0 ± 3.0A37.4 ± 10.3A70.6 ± 8.1A100.6 ± 12.3A,B
Palusplain101 ± 13.7A14.3 ± 8.3A28.4 ± 8.0A,B70.8 ± 17.8A131.3 ± 11.9A
Paluslope63.1 ± 6.3B1.4 ± 0.7A2.5 ± 1.2B49.5 ± 7.5A77.7 ± 8.3B
Peat typeUnburnedStrongModerateWeakNil
Fibric68.2 ± 8.0A7.2 ± 1.9A20.7 ± 4.8A50.2 ± 7.7A98.3 ± 13.4A
Hemic79.7 ± 5.8A,B0.1 ± 0.1A5.6 ± 2.3B64.4 ± 7.2A,B89.5 ± 7.9A
Sapric115.7 ± 11.3B23.1 ± 7.0B101.7 ± 11.6C114.5 ± 19.5Bn.d.

Values for the ‘Unburned’ column were modelled using binomial generalised linear models. Moistures expressed as percentages (g H2O/100 g dry soil), with mean and standard errors shown. Different letters represent significant differences across geomorphologies or peat types within the same combustion class.

Fig. 3.

Probability of peat samples combusting (a, b) or percentage mass loss of samples (c, d) as gravimetric moisture content increased compared across geomorphologies (a, c) and peat types (b, d). Binomial generalised linear models were used to calculate the probability that a sample would demonstrate moderate to strong combustion, as per Table 1. Percentage mass loss calculated using measurements taken before and after combustion trials.


WF24204_F3.gif
Table 3.Mean and standard error of mass loss for each combustion class in combined data or across geomorphologies and peat types.

CombinedStrongModerateWeakNil
Data combined8.2 ± 2.543.1 ± 9.062.2 ± 5.7100.2 ± 9.0
GeomorphologyStrongModerateWeakNil
Basin46.1 ± 3.7A20.1 ± 1.3A,B13.4 ± 1.5A10.3 ± 0.7A
Palusplain35.3 ± 4.6A,B11.9 ± 1.7A9.5 ± 0.8A,B10.7 ± 1.8A
Paluslope29.5 ± 4.0B26.2 ± 3.8B9.6 ± 0.5B11.7 ± 1.8A
Peat typeStrongModerateWeakNil
Fibric39.7 ± 2.3A17.9 ± 1.8A10.5 ± 0.7A9.3 ± 1.0A
Hemic24.2 ± 4.8B24.9 ± 3.8A9.8 ± 0.5A12.5 ± 1.7A
Sapric61.1 ± 5.2C19.0 ± 0.6A22.0 ± 3.0Bn.d.

Mass loss is expressed as % weight (g H2O/100 g dry mass). Different letters represent significant differences across geomorphologies or peat types within the same combustion class.

Binomial modelling predicted sapric peats would require 115.7% gravimetric moisture to resist sustained combustion, significantly more than fibric peats (68.2%) (P = 0.006, F = 3.585), but not hemic peats (79.7%) (Table 2, Fig. 3b). No sapric samples were classified as nil combustion, thus the moisture content required to prevent ignition was not determined for sapric peats. Gravimetric moisture to prevent ignition was measured as 89.5% for hemic peat and 98.3% for fibric peat (Table 2, Fig. 3b). Sapric peat types also lost significantly more mass than hemic or fibric types in strong and weak combustion classes (Table 3, Fig. 3d, P < 0.001, t ratio = 4.59).

Peat type predicted combustion more accurately than geomorphology when either was combined with gravimetric moisture content in proportional odds logistic regression (OLR) GAMs. EC, total organic carbon (TOC) and total nitrogen also improved models based on moisture alone (Fig. 4). The most accurate and parsimonious model combined gravimetric moisture, peat type and EC (Table 4), with a 28% greater predictive ability than pre-burn moisture alone. When peat types were modelled separately, pre-burn moisture was sufficient to predict combustion class for sapric peats, but EC improved the models for hemic and fibric peats by 4.7 and 18.5%, respectively (Table 4).

Fig. 4.

Partial dependence plots demonstrating influence of best performing factors to predict combustion class as per Table 4. (a) Role of peat types in model c.mp. (b) Role of electrical conductivity (EC) in model c.me. (c) Role of nitrogen (N) in model c.mn. (d) Role of EC in best-performing model, c.mep.


WF24204_F4.gif
Table 4.Best models to predict combustion classification compared using ordinal logistic regression generalised additive models.

Peat TypeModel idFactorsSmoothkDeviance explainedNagelkerke R2AICBIC
All combinedc.mPre-burn moisturetpmin34.60.686302.21320.32
c.mg+Geomorphologyremin47.10.822269.87293.01
c.mp+Peat typetp350.40.851262.27286.36
c.mt+TOCtp344.10.794278.61301.89
c.mn+Ntp350.50.852261.96285.58
c.me+ECtp353.60.876253.78277.45
c.mep+EC + Peat typetp354.20.881254.89282.66
Saprics.mPre-burn moisturetpmin99.4113.1815.75
s.mt+TOCtpmin99.4112.9415.45
s.me+ECtpmin99.4112.915.4
Hemich.mPre-burn moisturetpmin64.90.947100.23114.23
h.mt+TOCtp380.90.99280.89100.97
h.me+ECtp380.90.99280.82100.92
Fibricf.mPre-burn moisturetpmin35.40.692150.94161.69
f.me+TOCtp347.40.823141.07158.52
f.mt+ECtp347.10.82141.11158.34

Models were tested for each variable in combination with gravimetric moisture content on combined data. Best perfoming variables were subsequently tested in combinations for parsimony and accuracy on combined data and data broken into peat types, as per Australian Soil Classification Guidelines. Thin-plate (tp) splines were used except for Geomorphology, which had a random effect (re) spline applied. Best models defined by Bayesian Information Criterion (BIC) are highlighted in light grey. TOC: total organic carbon, N: nitrogen, EC: electrical conductivity, k: spline knots, min: minimal, AIC: Aikake information criterion.

Average values for several physical and chemical attributes varied significantly across peat types (Supplementary Table S2). Fibric peat samples had 25–36% higher acid neutralising capacity, 18–22% lower bulk density and 5–39% higher clay content than hemic or sapric peat samples. Hemic peat samples had 19–28% lower silt content, 14–19% lower coarse sand and approximately 22% more fine sand than fibric or sapric peat samples. Sapric peat samples had 54–85% greater EC, 31–47% more total carbon and 85–112% more total nitrogen than fibric or hemic peat samples.

A strong relationship (R2 = 0.86) was found between soil probe moisture readings and gravimetric soil moisture calculated using samples collected immediately after probe readings (Fig. 5a). The best fit line describing this relationship had an R-squared of 0.86 and equation y = 6.4374x + 0.0347, where y is the gravimetric moisture and x the soil probe moisture reading. This equation was used to calculate the soil probe readings required to prevent ignition or sustained combustion (Table 5; Fig. 5b). The data suggests a soil probe reading of 18% would be sufficient to minimise or negate peat combustion.

Fig. 5.

Soil moisture readings required to prevent peat from combusting. (a) Relationship between gravimetric soil moisture content and field measurements of the same samples using an Acclima SDI-12 Sensor. The R-squared value and equation for a line fitted to the points is shown. (b) Soil probe readings corresponding to threshold gravimetric moisture for peat types to remain unburned. Probe readings were calculated using the equation in A with mean plus standard errors in Table 2 ‘Unburned’ column. Actual values provided above respective bars.


WF24204_F5.gif
Table 5.Moisture thresholds in peat samples and associated soil probe readings to prevent ignition or minimise the risk of sustained combustion across geomorphologies and peat types.

GeomorphologyMoisture threshold – nil ignition (%)Probe value – nil ignition (%)Moisture threshold – minimal combustion (%)Probe value – minimal combustion (%)
Basin80.612.570.611.0
Palusplain10115.770.811.0
Paluslope63.19.849.57.7
Peat typeMoisture threshold – nil ignition (%)Probe value – nil ignition (%)Moisture threshold – minimal combustion (%)Probe value – minimal combustion (%)
Fibric68.210.650.27.8
Hemic79.712.364.410.0
Sapric115.718.0114.517.8

Peat moisture thresholds taken from Table 2, and probe moisture values calculated using the formula in Fig. 5. All values expressed as percentage moisture (g H2O/100 g dry soil).

Discussion

This study found that peat type, more than geomorphology, significantly affected gravimetric moisture thresholds to minimise combustion or prevent ignition. Moisture content was able to predict combustion to over 85% accuracy when peat type was known. Adding EC of soil improved the overall model and models for fibric and hemic peats, but no other factors improved combustion models for sapric peat types. This suggests that physical and chemical factors previously found to influence critical moisture thresholds may be inherently aligned with peat classification. These findings improve the ability to operationally assess the vulnerability of peats to ignition in a field setting, without supplementary laboratory testing.

Peat types are designated using physical tests to determine the degree of constituent plant matter decomposition, or humification, which would reflect other factors known to influence critical moisture thresholds (Taufik et al. 2019). As plant material decomposes from identifiable material to indistinguishable components, particle size decreases, allowing greater soil compaction and less soil porosity that would translate as higher soil bulk density (Collinson and Scott 1987; Davies et al. 2013). The percentage of organic content is higher in more decomposed substrates, such as sapric peats compared to fibric peats, or peats formed from less woody material like sphagnum moss compared to more fibrous sedges (Collinson and Scott 1987; Semeniuk and Semeniuk 2005; Hu et al. 2018; Taufik et al. 2019). Higher organic content increases the probability that smouldering combustion will occur at higher gravimetric moisture contents (e.g. Prior et al. 2020), thus increasing critical moisture thresholds needed to prevent ignition or sustained combustion. The heat of combustion is also directly related to organic content (Shafizadeh and Sekiguchi 1984). Peat types that have higher organic content would likely produce more heat during initial combustion increasing the likelihood that any moisture within peats would evaporate (Huang and Rein 2014), supporting a longer duration and further spread of smouldering combustion (Huang and Rein 2017).

Bulk density has shown a direct relationship to critical moisture thresholds. More porous peats must be drier than denser peats to ignite (Schulte et al. 2019; Prior et al. 2020). Porous materials are able to bond water in hygroscopic form, creating a thin surface film that must be vapourised to be removed (Huang and Rein 2014). Studies on Indonesian peatlands found that more humidified peats (i.e. sapric and hemic) retained less water (Taufik et al. 2019), decreasing the amount of evaporation required. Evaporation is an essential first step before pyrolysis and char formation can occur, while the latter two can occur simultaneously (Shafizadeh and Sekiguchi 1984; Huang and Rein 2014). The greater heat generated through pyrolysis and char formation compared to an external stimulus, like a heating coil, has influenced several studies to use a dry conduit such as smouldering charcoal or dry peat between the heating coil and peat samples being tested for critical moisture content (Frandsen 1997; Prat-Guitart et al. 2016; Pastor et al. 2017; Bates 2018; Prior 2020).

In this study we applied the heating coil directly to peat samples without a dry intermediary and found that sapric peats required significantly higher moistures to resist ignition compared to fibric or hemic peats. Sapric peats had the highest carbon and nitrogen contents and EC, indicating more organic matter. Although adding EC slightly improved the ability of models to predict combustion of fibric and hemic peats, EC values were not statistically different between fibric and hemic peats. Sapric peats also had significantly higher bulk density than fibric, but not hemic, peats. Fibric peats had the highest acid neutralising capacity, indicating significantly higher calcium carbonate content (Semeniuk and Semeniuk 2005), which may have decreased the moisture required to prevent ignition or sustained combustion (Semeniuk and Semeniuk 2005). Notably, moisture thresholds did not differ significantly between fibric and hemic peats at any combustion class, but the moisture required to prevent sustained combustion after ignition (e.g. weak combustion class) in hemic peats was statistically intermediate between fibric and sapric peats. Further testing with larger sample sizes is needed to establish whether this trend is significant.

This study was designed to test potential differences between geomorphologies rather than peat type, thus using the latter to model combustion probabilities had fewer samples than originally intended. Sites representing different geomorphologies could be chosen prior to sampling, unlike peat type, which required physical examination of samples. The act of sampling peat in Western Australia required a disturbance permit, thus constraining the design of this study. Moisture thresholds to prevent ignition or sustained smouldering combustion did differ significantly for palusplains relative to basins and paluslopes, but modelling showed peat type was a more accurate predictor of moisture thresholds than geomorphology. In addition, several peatlands selected and sampled failed to show sustained combustion even in dry soils, and were predominantly found to have carbon contents below 10%. Some sites did show evidence of prior fire damage, which may have caused degradation of the peats (Turetsky et al. 2015). Although this reduced the number of peat sites to be tested, using peat type reduced the groups from four to three, thus compensating for some loss of sites. Further, BIC were used during modelling to address small sample sizes, and this study’s findings make ecological sense. However, we recommend follow-up testing to confirm the hypothesis that peat type will affect critical moisture content.

Previous studies have identified multiple factors affecting ignition and sustained combustion of peat, andrequire further testing of peat samples often using specialised equipment. This presents a difficulty to fire managers who may lack the time and resources necessary to determine the gravimetric moisture, bulk density, organic and inorganic contents of peats. Source of peat has also been found to affect moisture thresholds (e.g. see Huang and Rein 2014, 2017 versus Lin et al. 2024). Testing flammability of peat turves from various sources have identified moisture, organic matter, inorganic content and bulk density to affect the probability of ignition, but critical moisture thresholds vary (e.g. Frandsen 1997; Reardon et al. 2007; Davies et al. 2013; Watts 2013; Prat-Guitart et al. 2016; Pastor et al. 2017; Prior et al. 2020).

This study has identified that peat type may be sufficient to determine critical moisture thresholds to prevent combustion in temperate ecosystems, which has considerable implications for operational use. Peat type can be classified fairly easily using the Australian Soil Classification guidelines (Isbell and National Committee on Soil and Terrain 2024 “Peat” in ASC-Glossary 1993). Robust relationships are also evident between soil moisture probes and gravimetric moisture (Prior et al. 2020, this study). These findings have importance to translating basic science into applied functionality, at least within southwest Western Australia. Future research to examine whether these principles apply more broadly may have great benefit to peatland conservation, particularly in light of changing rainfall patterns due to climate change.

Supplementary material

Supplementary material is available online.

Data availability

The data that support this study will be shared upon reasonable request to the corresponding author.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Declaration of funding

This research did not receive any specific funding.

References

Bates BC, McCaw L, Dowdy AJ (2018) Exploratory analysis of lightning-ignited wildfires in the Warren Region, Western Australia. Journal of Environmental Management 225, 336-345.
| Crossref | Google Scholar | PubMed |

Beaulne J, Garneau M, Magnan G, Boucher É (2021) Peat deposits store more carbon than trees in forested peatlands of the boreal biome. Scientific Reports 11, 2657.
| Crossref | Google Scholar | PubMed |

Benscoter BW, Thompson DK, Waddington JM, Flannigan MD, Wotton BM, de Groot WJ, Turetsky MR (2011) Interactive effects of vegetation, soil moisture and bulk density on depth of burning of thick organic soils. International Journal of Wildland Fire 20, 418-429.
| Crossref | Google Scholar |

Benson D, Baird IRC (2012) Vegetation, fauna and groundwater interrelations in low nutrient temperate montane peat swamps in the upper Blue Mountains. Cunninghamia 12, 267-307.
| Crossref | Google Scholar |

Burrows N, McCaw L (2013) Prescribed burning in southwestern Australian forests. Frontiers in Ecology and Environment 11, e25-e34.
| Crossref | Google Scholar |

Collinson ME, Scott AC (1987) Implications of vegetational change through the geological record on models for coal-forming environments. Geological Society Special Publication 32, 67-85.
| Crossref | Google Scholar |

Davies GM, Gray A, Rein G, Legg CJ (2013) Peat consumption and carbon loss due to smouldering wildfire in a temperate peatland. Forest Ecology and Management 308, 169-177.
| Crossref | Google Scholar |

Department of Environment and Conservation (2012) Wetland vegetation and flora, part 4: Southwest. In ‘A guide to managing and restoring wetlands in Western Australia’. Prepared by G Keighery, B Keighery, V Longman. (Department of Environment and Conservation: Perth, Western Australia)

Dowdy AJ, Kuleshov Y (2014) Climatology of lightning activity in Australia: spatial and seasonal variability. Australian Meteorological and Oceanographic Journal 64, 103-108.
| Crossref | Google Scholar |

Frandsen WH (1987) The influence of moisture and mineral soil on the combustion limits of smoldering forest duff. Canadian Journal of Forest Research 17, 1540-1544.
| Crossref | Google Scholar |

Frandsen WH (1997) Ignition probability of organic soils. Canadian Journal of Forest Research 27, 1471-1477.
| Crossref | Google Scholar |

Gore AJP (1983) ‘Ecosystems of the World 4A. Mires: Swamp, Bog, Fen, and Moor.’ (Elsevier Scientific Publishing Company: Amsterdam, Netherlands)

Greenwell BM (2017) pdp: An R Package for Constructing Partial Dependence Plots. The R Journal 9, 421-436.
| Crossref | Google Scholar |

Grishin AM, Yakimov AS, Rein G, Simeoni A (2009) On physical and mathematical modelling of the initiation and propagation of peat fires. Journal of Engineering Physics and Thermophysics 82, 1235-1243.
| Crossref | Google Scholar |

Horwitz P (1997) Comparative endemism and richness of the aquatic invertebrate fauna in peatlands and shrublands of far south-western Australia. Memoirs of the Museum of Victoria 56, 313-321.
| Crossref | Google Scholar |

Horwitz P, Sommer B, Hewitt P (2009) Chapter Five: Wetlands–Changes, Losses and Gains. In ‘Biodiversity Values and Threatening Processes of the Gnangara Groundwater System.’ (Eds BA Wilson, LE Valentine) pp. 225–267. (Department of Environment and Conservation: Perth, Western Australia)

Howard T, Burrows N, Smith T, Daniel G, McCaw L (2020) A framework for prioritising prescribed burning on public land in Western Australia. International Journal of Wildland Fire 29, 314-325.
| Crossref | Google Scholar |

Hu Y, Fernandez-Anez N, Smith TEL, Rein G (2018) Review of emissions from smouldering peat fires and their contribution to regional haze episodes. International Journal of Wildland Fire 27, 293-312.
| Crossref | Google Scholar |

Huang X, Rein G (2014) Smouldering combustion of peat in wildfires: inverse modelling of the drying and the thermal and oxidative decomposition kinetics. Combustion and Flame 161, 1633-1644.
| Crossref | Google Scholar |

Huang X, Rein G (2015) Computational study of critical moisture and depth of burn in peat fires. International Journal of Wildland Fire 24, 798-808.
| Crossref | Google Scholar |

Huang X, Rein G (2017) Downward spread of smouldering peat fire: the role of moisture, density and oxygen supply. International Journal of Wildland Fire 26, 907-918.
| Crossref | Google Scholar |

Huang X, Rein G, Chen H (2015) Computational smouldering combustion: predicting the roles of moisture and inert contents in peat wildfires. Proceedings of the Combustion Institute 35, 2673-2681.
| Crossref | Google Scholar |

IPCC (2021) Working Group I. Sixth assessment report – Regional fact sheet – Australasia. Available at https://www.ipcc.ch/report/ar6/wg1/downloads/factsheets/IPCC_AR6_WGI_Regional_Fact_Sheet_Australasia.pdf

Isbell RF, National Committee on Soil and Terrain (2024) “Peat” in ASC-Glossary (1993) The Australian Soil Classification, 3rd edn online. Available at https://www.soilscienceaustralia.org.au/asc/soilglos.htm#be [verified 11 March 2024].

Jones W (2005) Peat fires: the dangers from a fire manager’s point of view. Journal of the Royal Society of Western Australia 88, 139-142.
| Google Scholar |

Lenth R (2025) _emmeans: Estimated Marginal Means, aka Least-Squares Means_. doi:10.32614/CRAN.package.emmeans

Lin S, Zhang T, Huang X, Gollner MJ (2024) The initiation of smouldering peat fire by a glowing firebrand. International Journal of Wildland Fire 33, WF23116.
| Crossref | Google Scholar |

McDougall KL, Whinam J, Coates F, Morgan JW, Walsh NG, Wright GT, Hope GS (2023) Fire in the bog: responses of peatland vegetation in the Australian Alps to fire. Australian Journal of Botany 71, 111-126.
| Crossref | Google Scholar |

Moore PD (1987) Ecological and hydrological aspects of peat formation. Geological Society, London, Special Publications 32, 7-15.
| Crossref | Google Scholar |

Nakazawa M (2024) _fmsb: Functions for Medical Statistics Book with some Demographic Data. R package version 0.7.6. Available at https://CRAN.R-project.org/package=fmsb

Page SE, Siegert F, Rieley JO, Boehm HDC, Jaya A, Limin S (2002) The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61-65.
| Crossref | Google Scholar | PubMed |

Pastor E, Oliveras I, Urquiaga-Flores E, Quintano-Loayza JA, Manta MI, Planas E (2017) A new method for performing smouldering combustion field experiments in peatlands and rich-organic soils. International Journal of Wildland Fire 26, 1040-1052.
| Crossref | Google Scholar |

Pemberton M (2005) Australian peatlands: a brief consideration of their origin, distribution, natural values and threats. Journal of the Royal Society of Western Australia 88, 81-89.
| Google Scholar |

Prat-Guitart N, Rein G, Hadden RM, Belcher CM, Yearsley JM (2016) Propagation probability and spread rates of self-sustained smouldering fires under controlled moisture content and bulk density conditions. International Journal of Wildland Fire 25, 456-465.
| Crossref | Google Scholar |

Prior LD, French BJ, Storey K, Williamson GJ, Bowman DMJS (2020) Soil moisture thresholds for combustion of organic soils in western Tasmania. International Journal of Wildland Fire 29, 637-647.
| Crossref | Google Scholar |

Rayment GE, Higginson FR (1992) ‘Australian Laboratory Handbook of Soil and Water Chemical Methods.’ (Inkata Press: Melbourne, Australia)

R Core Team (2024) R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing: Vienna, Austria) Available at https://www.R-project.org/

Reardon J, Hungerford R, Ryan K (2007) Factors affecting sustained smouldering in organic soils from pocosin and pond pine woodland wetlands. International Journal of Wildland Fire 16, 107-118.
| Crossref | Google Scholar |

Rein G, Cleaver N, Ashton C, Pironi P, Torero JL (2008) The severity of smouldering peat fires and damage to the forest soil. Catena 74, 304-309.
| Crossref | Google Scholar |

Rundel PW, Arroyo MT, Cowling RM, Keeley JE, Lamont BB, Vargas P (2016) Mediterranean biomes: evolution of their vegetation, floras, and climate. Annual Review of Ecology, Evolution, and Systematics 47, 383-407.
| Crossref | Google Scholar |

Semeniuk CA, Semeniuk V (1995) A geomorphic approach to global classification for inland wetlands. Vegetation 118, 103-124.
| Crossref | Google Scholar |

Semeniuk Research Group (1997) Mapping and classification of wetlands from Augusta to Walpole in the south west of Western Australia. Water Resource Technical Series Report 12. Water and Rivers Commission, Policy and Planning Division, Perth, WA. Available at https://nla.gov.au/nla.cat-vn1625608

Semeniuk V, Semeniuk CA (2005) Wetland sediments and soils on the Swan Coastal Plain, southwestern Australia: types, distribution, susceptibility to combustion, and implications for fire management. Journal of the Royal Society of Western Australia 88, 91-120.
| Google Scholar |

Schulte ML, McLaughlin DL, Wurster FC, Varner JM, Steward RD, Aust WM, Jones CN, Gile B (2019) Short- and long-term hydrologic controls on smouldering fire in wetland soils. International Journal of Wildland Fire 28, 177-186.
| Crossref | Google Scholar |

Shafizadeh F, Sekiguchi Y (1984) Oxidation of chars during smoldering combustion of cellulosic materials. Combustion and Flame 55, 171-179.
| Crossref | Google Scholar |

Taufik M, Veldhuizen AA, Wosten JHM, van Lanen HAJ (2019) Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard. Geoderma 347, 160-169.
| Crossref | Google Scholar |

Turetsky MR, Benscoter B, Page S, Rein G, van der Werf GT, Watts A (2015) Global vulnerability of peatlands to fire and carbon loss. Nature GeoScience 8, 11-14.
| Crossref | Google Scholar |

Watts AC (2013) Organic soil combustion in cypress swamps: moisture effects and landscape implications for carbon release. Forest Ecology and Management 294, 178-187.
| Crossref | Google Scholar |

Whinam J, Hope G (2005) The peatlands of the Australasian region. Stapfia 85, 397-433.
| Google Scholar |

Wickham H (2016) ‘ggplot2: Elegant Graphics for Data Analysis.’ (Springer-Verlag: New York, NY, USA)

Winkle H, Horwitz P, Blake D, Tauss C (2021) Empodisma gracillimum based peatlands of southwestern Australia. Available at https://www.auricht.com/awi/documents/peat/Revised_Empodisma_Literature_Review.pdf [verified 26 November 2024]

Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73, 3-36.
| Crossref | Google Scholar |