IntroductionTop

The recognition of the significant influence of fuel moisture content (M) on fire rate of spread (R) is as old as fire research itself (e.g., Show, 1919). The physical mechanisms underlying the substantial R decrease with the M-growth are quite simple to understand: as M increases so does the amount of energy and time necessary for water vaporization before ignition can be achieved, slowing down fire-spread.

Because natural mixed live and dead vegetation is difficult to reproduce indoors, laboratory studies have seldom been focused on fire spread in live fuel beds. The analysis of fire behaviour in fuel complexes including live components is usually derived from field studies. However, although counterintuitive, a review by Alexander & Cruz (2013) found no statistically significant relationship between field fires R and live fuel moisture content (Ml). Flammability tests on individual live fuel elements subjected to high radiative heat fluxes (up to 140 kW/m2) suggest a weak relationship between Ml and time to ignition (Fletcher et al., 2007) that occurs before all moisture is vaporized (Picket et al., 2010). Not surprisingly, such findings support the belief that fire-spread mechanisms in live vegetation differ from those observed in dead fuels (Finney et al., 2013). Another plausible explanation for the apparent lack of influence of Ml on R, which does not require a priori different spread mechanisms, would be that dead fuel elements allow fire front percolation through mixed live and dead vegetation.

In contrast, most laboratory studies find a relationship between R and Ml (e.g., Rossa et al., 2016; Weise et al., 2016). As a consequence, R models derived from such studies are commonly based on a weighted fuel moisture content (Mw), computed from live and dead fuel mass fractions (e.g., Marino et al., 2012; Viegas et al., 2013), whereas field-based models rely on dead fuel moisture content (Md) alone (e.g., Cruz et al., 2015). Conclusions on the Ml effect derived from field and laboratory studies are thus conflicting.

Odds that the results from such an amount of high quality laboratory and fieldwork are somehow wrong are virtually null. Instead, we believe that the most viable explanation for this dispute is that all results are correct, but a unifying theory is lacking. This study discusses whether apparently different outcomes regarding the Ml effect on R, derived from either laboratory or field studies, are reconcilable.

MethodsTop

Fuel moisture content effect

Shrublands are typically composed of a variable live and dead fuel mixture, and thus adequate to the present analysis. Anderson et al. (2015) developed an empirical R model from a comprehensive set of 79 field experiments of fire spread in shrublands worldwide. The M-effect was modelled as an exponential decay based only on dead fuels, given by exp(-0.0721 Md). To obtain a measure of how the established important influence of wind speed (U) on R might hinder a proper field evaluation of the M-effect, we assessed the relative effects of U and Md using daily R simulations for a two-year series (2015–2016) of meteorological observations recorded at 12:00 in Lousã, Central Portugal. Md was estimated from the Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire Weather Index System (Van Wagner, 1987). We did not consider days with Md >35% (because of unlikely fire spread) and used a bulk density of 1.8 kg m-3 (mean experimental value). U and Md relative contributions to R were assessed using classification and regression trees (CART) analysis.

Rossa & Fernandes (2017) obtained an M-damping effect based on total fuel bed water content (live and dead fuels), given by Mw-0.6253, using 51 fire spread laboratory tests in fuel beds composed of a litter layer (dead foliage) over-layered by vertically oriented quasi-live fuels, thus approaching the natural fuel structure. Mw can be obtained from:

where fd is the mass fraction of dead foliar fuels. Because both the Anderson et al. (2015) field-based model and the Rossa & Fernandes (2017) laboratory-based model include fuel bed density and were derived from wind-driven fire spread, we assumed that the M-effect is comparable.

Fuel moisture content evolution throughout the year

We compared the laboratory and field-based M-damping effects for fire spread in a mixed Calluna vulgaris (L.) Hull and Pterospartum tridentatum (L.) Wilk. fuel complex, common in N-C Portugal shrubland, for the typical M-evolution throughout the year. We used monthly averages from a long term (1996–2012) M-assessment (Lopes, 2013) of a few selected fuel species, conducted in Lousã. Md was obtained as the mean value between Pinus pinaster Ait. and Eucalyptus globulus Labill. litter, and Ml as the mean between live C. vulgaris and P. tridentatum, which assumes an equally distributed mixture. Mw was computed for fd of, respectively, 0.2, 0.4, and 0.6 – i.e., 20, 40, and 60% of fine dead fuels – and we assessed the significance (p<0.05) and strength (R2) of the linear relationship between the two M-damping effects. The range in fd for Anderson et al. (2015) model development was 0.14–0.91 (mean was 0.49), thus confirming that the selected values of fd for analysis are realistic.

Results and discussionTop

Laboratory and field studies results

The U effect dominated over Md, respectively explaining 61.9 and 38.1% of the variance in R predictions. The prevalence of such effect would probably be higher had we resorted to simulations based on hourly weather data, because of higher variation in U (Beck & Trevitt, 1989). In the year-round experimentally-assessed M evolution, Md and Ml varied within 12.8–52.6% and 76.0–108.2% (Fig. 1). The linear relationship between laboratory and field-based M-damping effects (Fig. 2) approached significance for fd=0.2 (p=0.06) and was highly significant for fd=0.4 (p=0.004) and fd=0.6 (p<0.0001). Likewise, R2 increased markedly from 0.318 to 0.813 with the rise of fd.

Laboratory testing allows the control and/or accurate monitoring of the main parameters influencing fire propagation. Rossa (2017) used data from 185 burns under windless conditions in the absence of slope, covering a wide diversity of fuel bed composition, arrangement and M (6–179%) conditions, showing that the M-effect on R does not depend on vegetation condition (live or dead). Extension of the results to real-world fires was confirmed by model validation against field fires. In the case of slope (e.g., Rossa et al., 2016) or wind-driven (e.g., Marino et al., 2012; Rossa & Fernandes, 2017) laboratory trials, R is limited by the fire front width (Fernandes et al., 2009). Still, these trials are representative of the initial stage of a point ignition field fire and the M-damping effect, as well as the independence from plant phenology, are expected to be independent of scale and hold under field conditions (Rossa et al., 2016).

Outdoors experimental fires occur in real-world conditions. No fire behaviour model can be completely proven correct until it faces validation against field fires. Their use as a source of development data is also appealing because there are many fire-spread situations that cannot be reproduced in the laboratory without serious scaling limitations. Yet, this option is challenged by lack of control over environmental parameters, heterogeneity in fuel bed properties, and correlated fuel descriptors. These shortcomings have been fostering the dispute over the Ml influence on R, not clarifying if it is eluded by the difficulty in detecting specific effects, or if it is diluted by field-specific spread mechanisms. The latter remains to be verified only for the case of slope and wind-driven fires, since for no-wind or no-slope burns – which also approach backing fires (Rossa et al., 2015) –, the accuracy of Mw-based R predictions was already tested against field fires, confirming the Ml influence (Rossa, 2017).

Unifying laboratory and field evidence

Ml will be approximately constant during a field experimental program in shrubland fuels conducted over a period of a few weeks, contrasting with Md that will vary substantially in response to changes in air temperature, relative humidity and solar radiation (Anderson et al., 2015). Other types of live vegetation, like evergreen mature tree foliage without severe soil water deficit, will even usually maintain an approximately constant Ml over the year (Pook & Gill, 1993; Agee et al., 2002). However, for the shrubs addressed by this study, the difference between minimum and maximum Ml was 32.1% for average monthly means (Fig. 1), a value similar to that observed for Md (39.8%). Nevertheless, because of similar seasonal trends for Ml and Md, which are evident in Fig. 1, Md and Mw-based damping effects are correlated (Fig. 2).

Although the Ml evolution in Fig. 1 is not necessarily representative of a general shrubland, several year-round Mediterranean Ml assessments for a great number of shrub species (Piñol et al., 1998; Pellizzaro et al., 2007; Yebra et al., 2008; Yebra & Chuvieco, 2009), report a similar seasonal pattern, with a Ml decrease between spring and summer, followed by an increase until winter. Because dry soils are frequently concurrent with meteorological conditions that yield low Md values as well, the relationship between Ml and soil moisture content found by Qi et al. (2012) for a shrubland in Northern Utah (USA), also lends support to our hypothesis of correlated Md and Mw damping effects.

Both cases of: (i) a year-round roughly constant Ml, or (ii) correlated Md-Mw variations, explain why the Ml-effect is difficult to detect from the statistical analysis of field data, although laboratory studies systematically show that R is influenced by the total amount of water in the fuel complex, independently of vegetative condition. Dominance of R by U also adds to the challenge of properly assessing the M-effect based on field fires. The good agreement between the M-damping effects of field and laboratory-based R models also enlightens why the sole use of Md is enough to provide a satisfactory R explanation in most fuel-dependent models (e.g., Cruz et al., 2015). Differences between the combustion mechanisms of live and dead fuels also do not necessarily lead to different R, because if all water is evaporated during the passage of the flame (before or after ignition) the net heat release sustaining fire-spread should approximately be the same (Rossa, 2017).

ConclusionTop

There is laboratory evidence that the main effect of M on R is a function of the total amount of water in the fuel bed and is independent of vegetative condition. The apparently different effects of Ml, assessed from either field or laboratory fires, are most likely a consequence of field-specific experimental features and the results from both approaches can easily be reconciled. In the absence of severe soil water deficit many types of mature live vegetation will maintain an approximately constant Ml over the year. Although that is not the case for the Mediterranean shrublands analysed in this work, their mean monthly Ml and Md evolution trends are similar. As a result, the Ml-effect is very difficult to detect from field data. For the same reason, fuel-dependent R models provide satisfactory predictions relying on the sole use of Md.