Modelling the potential for prescribed burning to ... - CSIRO Publishing

CSIRO PUBLISHING

International Journal of Wildland Fire 2012, 21, 629–639 http://dx.doi.org/10.1071/WF11023

Modelling the potential for prescribed burning to mitigate carbon emissions from wildfires in fire-prone forests of Australia R. A. Bradstock A,B,L, M. M. Boer B,C,K, G. J. Cary B,D, O. F. Price A, R. J. Williams B,E, D. Barrett F, G. Cook E, A. M. Gill B,D, L. B. W. Hutley G, H. Keith D, S. W. Maier G, M. Meyer H, S. H. Roxburgh I and J. Russell-Smith J A

Centre for Environmental Risk Management of Bushfires, University of Wollongong, NSW 2522, Australia. B Bushfire Cooperative Research Centre, East Melbourne, Vic. 3002, Australia. C Ecosystems Research Group, School of Plant Biology M090, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. D Fenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia. E CSIRO Ecosystems Sciences, CSIRO Climate Adaptation Flagship and CSIRO Sustainable Agriculture Flagship, PMB 44 Winnellie, NT 0822, Australia. F Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, Qld 4072, Australia. G School of Environmental and Life Sciences, Charles Darwin University, Darwin, NT 0909, Australia. H CSIRO Marine and Atmospheric Research, Aspendale, Vic. 3195, Australia. I CSIRO Sustainable Agriculture Flagship and CSIRO Ecosystems Sciences, GPO Box 284, ACT 2601, Australia. J Bushfires NT, Winnellie, NT 0820, Australia. K Present address: Hawkesbury Institute for the Environment – University of Western Sydney, Richmond, 2753 NSW, Australia. L Corresponding author. Email: [email protected]

Abstract. Prescribed fire can potentially reduce carbon emissions from unplanned fires. This potential will differ among ecosystems owing to inherent differences in the efficacy of prescribed burning in reducing unplanned fire activity (or ‘leverage’, i.e. the reduction in area of unplanned fire per unit area of prescribed fire). In temperate eucalypt forests, prescribed burning leverage is relatively low and potential for mitigation of carbon emissions from unplanned fires via prescribed fire is potentially limited. Simulations of fire regimes accounting for non-linear patterns of fuel dynamics for three fuel types characteristic of eucalypt forests in south-eastern Australia supported this prediction. Estimated mean annual fuel consumption increased with diminishing leverage and increasing rate of prescribed burning, even though average fire intensity (prescribed and unplanned fires combined) decreased. The results indicated that use of prescribed burning in these temperate forests is unlikely to yield a net reduction in carbon emissions. Future increases in burning rates under climate change may increase emissions and reduce carbon sequestration. A more detailed understanding of the efficacy of prescribed burning and dynamics of combustible biomass pools is required to clarify the potential for mitigation of carbon emissions in temperate eucalypt forests and other ecosystems. Additional keywords: Eucalyptus, fire management, fire regimes, fuel. Received 9 February 2011, accepted 24 January 2012, published online 5 July 2012

Introduction Fires mediate ecosystem structure, function and interactions between the terrestrial biosphere and atmosphere (Bowman et al. 2009). In particular, the contribution of carbon Journal compilation Ó IAWF 2012

and greenhouse gases (GHG) from vegetation fires to global emissions is significant and has the potential to rise in the future (van der Werf et al. 2006; Bowman et al. 2009). www.publish.csiro.au/journals/ijwf

630

Int. J. Wildland Fire

Management practices that reduce emissions from wildfires may therefore play an important role in mitigating climatic change through reducing the amount of biomass that is burnt. Options for managed mitigation of emissions from wildfires, via prevention and suppression, involve restriction of the incidence, extent and intensity of fires. The potential for the managed mitigation of emissions from landscape fires requires exploration, particularly in a way that accounts for variations in fire regimes and vegetation characteristics among differing ecosystems. Management of fire regimes to mitigate emissions is conceptually straightforward. Fuel reduction, via thinning or prescribed fire, results in emissions, but can also decrease the intensity or extent of subsequent wildfires, leading to lower emissions over the long term. Thus an outlay in fuel treatment is required to derive a subsequent saving of emissions from wildfires. Mitigation of wildfires via the modification of fuel in forests of North America and Europe is predicted to have the potential to produce a major reduction in emissions (Narayan et al. 2007; Hurteau et al. 2008; Hurteau and North 2009; Wiedinmyer and Hurteau 2010). Such a prediction may not, however, apply to all forest types owing to differences in fuel, environmental and fire characteristics. A long-term reduction in emissions will not occur if emissions from fuel treatments exceed those saved from unplanned fires (Bradstock and Williams 2009; Mitchell et al. 2009; Campbell et al. 2011). The balance of emissions from fuel treatments and wildfires will be determined by the degree to which fuel treatments modify the incidence, size and intensity of unplanned fires. An integration of the efficacy of fuel treatment with emission estimates is therefore required to assess the potential for fuel treatments to mitigate emissions in any particular ecosystem (Campbell et al. 2011). Mitchell et al. (2009) demonstrated that potential for mitigation of emissions also depends on forests type, with potential higher for drier forests than wetter forests in the Pacific Northwest of the USA. Variations in ignition rates, fuel dynamics and weather conditions will also fundamentally affect fire regimes and their sensitivity to management practices (e.g. Keeley and Zedler 2009). Therefore, the potential to mitigate emissions may vary as a function of vegetation type and productivity and a host of related ecological factors, which may affect wildfire activity and the efficacy of management. Australia contains a range of fire-prone, temperate forests dominated by Eucalyptus species and other closely related genera (Gill and Catling 2002; Bradstock 2010). These forests occupy relatively moist environments around the coast and hinterlands in southern Australia. Owing to their make-up (grassy or shrubby understorey) and their climatic niche, these forest burn regularly (e.g. 5- to 30-year frequency in dry sclerophyll forest types; Boer et al. 2009; Price and Bradstock 2011). The potential to mitigate emissions from fires in these forests is therefore significant, but a comprehensive assessment is required to determine if this potential can be realised. Here, we propose a general, conceptual model for evaluating the potential of fire management to mitigate emissions using prescribed burning as a fuel treatment. This is based on the difference between the ‘outlay’ and ‘saving’ of emissions that may occur as a result of fuel treatment and diminished wildfire activity

R. A. Bradstock et al.

respectively, and variations in potential efficacy of prescribed burning. We then scrutinise predictions from the model by simulating the effects of different rates of prescribed burning on fuel consumption for temperate eucalypt forests. This yields direct insight into the potential to mitigate overall emissions of carbon, given that approximately half the mass of fuel comprises carbon. Methods Quantification of the efficacy of prescribed burning Quantification of the potential for mitigation of emissions from fires requires estimation of the effect of differing management activities on area burned and the consumption of biomass (a fundamental determinant of fire intensity). Management aimed at reducing fire activity and consequent emissions through manipulation of fuel using measures such as prescribed burning will require an outlay (i.e. prescribed fires will produce emissions) with a presumed return (i.e. reduction in emissions from subsequent unplanned fires). A net benefit will accrue only if the return (emissions reduced) exceeds the outlay (Bradstock and Williams 2009; Mitchell et al. 2009; Campbell et al. 2011). Although the potential effects of altered fuel levels on fire behaviour are reasonably well understood (Fernandes and Bothello 2003), effects of fuel treatments at large spatial and temporal scales, incorporating variable influences of weather, terrain and fuel and interactions, are less well studied. Fuel treatments are typically heterogeneous and their effectiveness is finite owing to the re-accumulation of fuel, which is non-linear in many ecosystems (Raison et al. 1983; Krivtsov et al. 2009). Thus, an understanding of the relationship between fuel treatment rate (i.e. percentage of area treated per annum), accumulation rates and unplanned fire activity (incidence, size and intensity) is needed to determine the potential for mitigation of emissions. The effectiveness of the rate of prescribed burning (P, percentage area of landscape treated per annum) in reducing the rate of burning by unplanned fires (U, percentage area of landscape burnt per annum) will vary as a function of fundamental properties of ecosystems such as weather, fuel, terrain and the capacity of suppression resources to exploit the lower fire intensities that result from treatment (Fernandes and Bothello 2003). The relationship between U and P can be expressed in terms of ‘leverage’ of prescribed burning (L, the area of unplanned fire reduced per unit of area of prescribed fire; Loehle 2004; Boer et al. 2009). L is therefore indicated by the slope of the relationship (i.e. dU/dP) between unplanned and prescribed fire rates (Fig. 1a). We define U* as the rate of burning by unplanned fire in the absence of prescribed burning (i.e. for P ¼ 0). Low values of L indicate relatively low effectiveness of prescribed fire in reducing U (Fig. 1a). A value of unity indicates exact substitution of unplanned fire area by prescribed fire. In this case, prescribed fire would totally supplant unplanned fire at a treatment rate equivalent to U* (Fig. 1a). The domain of L for a given environment will have fundamental consequences for the properties of the fire regime experienced at any point in the landscape. When L , 1 (e.g. L ¼ 0.25), a net increase in overall area burned (P þ U,

Mitigating carbon emissions with prescribed fire


0.17 0.25 0.50 1.00

Total area burnt (% area pa)

(b)

Unplanned burning rate U (% area pa)

(a)

631

0.25 0.50 1.00

Prescribed burning rate P (% area pa)


1.00 0.50

0.25 0.17


Mean intensity (kW m⫺1)

(d ) Mean inter-fire interval (years)

(c)

0.17

1.00 0.50

0.25

0.17


Fig. 1. Predicted trends in the responses of: (a) the rate of unplanned fires across a landscape; (b) total area burned in a landscape by prescribed plus unplanned fires; (c) overall length of inter-fire interval (IFI) at a point in a landscape and (d ) overall fire intensity at a point in a landscape (prescribed plus unplanned fires combined), to rate of prescribed burning (percentage area treated per annum). Predicted trends under differing values of prescribed fire leverage (L ¼ 1.0 to 0.17 range) are shown (smaller values of L represent lower prescribed fire efficacy).

Fig. 1b) will occur and a corresponding decrease in the length of inter-fire intervals experienced at a point (Fig. 1c). When L ¼ 1, the length of the average inter-fire interval (IFI) will, by definition, be unaffected by P (Fig. 1c). The average intensity (I ) of all fires (prescribed and unplanned) experienced at a point will decline with increasing P (Fig. 1d ) owing to the smaller fuel loads, on average, associated with shorter IFI. L also affects the rate at which I will decline with P, so that I will decline more rapidly at larger values of L (Fig. 1d ) because prescribed fires more readily dominate the fire regime when L is high.

occur, average fuel consumed per unit area by prescribed fires in a landscape must be less than fuel consumed by unplanned fires. This difference must be sufficiently large to outweigh any effect of an overall increase in area burned when L , 1 (Fig. 1b). Mitigation potential (i.e. the ratio of emissions from prescribed fire v. unplanned fire, E, net emissions ratio) is therefore defined by:

Potential mitigation of carbon emissions: a conceptual model Emissions of carbon by fire will be a function of the amount of fuel consumed. This involves an integration of the effects of prescribed fire on area burned as a function of leverage (Fig. 1b) and effects on fire frequency and intensity (Fig. 1c, d ), given that fire frequency will determine fuel accumulation. The amounts of fuel consumed per unit area by prescribed and unplanned fires (Bp and Bu respectively) will partly determine the potential for emission mitigation via use of prescribed fire. For mitigation to

R ¼ Bp =Bu

E ¼ R=L

ð1Þ

where R is defined as the relative fuel consumption: ð2Þ

Inherently, mitigation occurs when E , 1. When E . 1, there is an overall increase in emissions (Fig. 2). At values of Bp/Bu close to 1, high leverage L will be required to mitigate emissions (Fig. 2). Therefore, the potential for management to mitigate emissions in vegetation communities with naturally low fuel loads may be less than in communities with inherently high fuel loads (i.e. the range of potential fire intensity and fuel consumption is inherently smaller when fuel

632



P R Net emission ratio (E )

1.5 1.0 0.8

1.0

C

0.6 0.4

0.5

0.2

0.125

0.250

1.000

0.500

Leverage (L) Fig. 2. A conceptual model of potential mitigation of carbon emissions as a function of prescribed fire leverage (L). The net emissions ratio (E) is the ratio of potential emissions from prescribed fires to emissions from unplanned fires: values ,1 indicate a benefit (i.e. net reduction in emissions over time) whereas values .1 indicate an adverse outcome (i.e. a net increase in emissions over time). Contours on the graph show the relationship with differing ratios of fuel consumption (R, relative fuel consumption) of prescribed (P) v. unplanned (U) fires: i.e. where R ¼ BP/Bu. The solid arrow predicts the trend with increasing rate of prescribed fire (P) at any leverage, whereas the dashed arrow indicates the potential effect of climate change (C) in reducing leverage via an elevation in fire danger.

loads are low). When L ,, 1, the potential for mitigation by prescribed burning is limited and only possible when fuel consumption rates of prescribed fires are much smaller than those of unplanned fires (i.e. Bp ,, Bu, Fig. 2). Independently of L, we can expect the ratio Bp/Bu to increase with the prescribed burning rate P as fire intervals become shorter and the fuel consumption rates of prescribed and unplanned fires become increasingly similar. This implies that for a given value of L, emission reduction becomes increasingly difficult as the prescribed burning rate P is increased (Fig. 2). Simulation of prescribed burning and fuel consumption in temperate eucalypt forests In the temperate eucalypt forests of southern Australia, evidence from historical analyses and landscape simulation of area burned indicates that L is in the range of 0.25 to 0.33 (Boer et al. 2009; Price and Bradstock 2010, 2011; Bradstock et al. 2012). These studies encapsulate the effects of contemporary use of prescribed burning for fuel reduction over large spatial and temporal scales in Australian forests. Such burning practices involve targeting of key strategic areas in landscapes where fuel reduction can disrupt the spread of wildfires. Thus, on the basis of the conceptual model (Fig. 2), the potential to mitigate emissions through prescribed burning may be low in these forests. By contrast, in tropical savanna woodlands of northern Australia, empirical evidence (e.g. Gill et al. 2000) suggests that leverage occurs approximately at unity or higher (i.e. L $ 1),

indicating significant potential for mitigation of emissions through prescribed burning in the early dry season (RussellSmith et al. 2009a). The prediction of low potential for mitigation of emissions through prescribed burning in Australian temperate forests does not account for typical non-linear rates of fuel accumulation and their effects on potential fire intensity and fuel consumption (Fig. 3), or effects of wide variations in fuel accumulation rates among differing forest types (Raison et al. 1983). We therefore used a process-based simulation to further examine how variations in fuel accumulation rate (fuel type), leverage and rate of prescribed burning may affect fire intensity, fuel consumption and consequent potential for mitigation of emissions of carbon in eucalypt forests. We estimated consumption of aboveground pools of fuel as a general indicator of carbon emissions, based on the assumption that higher levels of consumption will equate to higher levels of emissions in general. We explicitly simulated: (i) the effects of leverage in determining the fire regime via effects of prescribed fire on the unplanned fire cycle and (ii) the consequent fuel consumption resulting from the interaction of the fire cycle with fuel dynamics. The overall effect of any particular fire regime on carbon emissions will be a direct function of average fuel consumption over time. Changes to fire regimes will alter average fuel consumption, along with the proportion of residual fuel that is decomposed in the absence of fire. This residual is the difference between average consumption by fire and the theoretical maximum equilibrium fuel load (Fig. 3).



633

30 Type lll

Fine fuel load (t ha⫺1)

Fuel (t ha⫺1)

25

Time (years) Fig. 3. Predicted trend in fuel load over time, under a regime of frequent, prescribed fires (dashed arrow) and infrequent unplanned fires (solid arrow), causing relatively small and large reductions in fuel load respectively. The non-linear trend in re-accumulation of fuel between fires is illustrated. The average level of fuel load under the fire regime depicted is indicated by the solid line, whereas the maximum, equilibrium fuel load (no fire) is indicated by the dashed line. The difference between the maximum (no fire) and long-term average trend under the fire regime indicates the potential amount of fuel that is decomposed rather than burnt if fire is absent.

20 Type l

Type ll 15

10

5

0 0

10

20

30

40

50

Time (years) Fig. 4. Accumulation models for surface litter in three fuel types (I, II and III) representative of temperate eucalypt forests in south-eastern Australia.

Fuel accumulation followed a typical non-linear Olson-type model (Raison et al.1983): We simulated the fire regimes that would occur at a point in the landscape in response to variations in rate of prescribed burning and leverage. Estimates of mean IFI, fire intensity (i.e. as in Fig. 1c, d ) and resultant fuel consumption were made for a representative 1-ha plot. Effects of prescribed burning in altering intensity of unplanned fires and overall fuel consumption were explicitly estimated in situ. In addition, the effects of prescribed burning across the landscape in altering the probability of fire arriving at the notional plot, and consequent withinplot IFI and intensity, were estimated for a wide range of leverage values (L ¼ 1, 0.5, 0.25, 0.17) transcending the measured contemporary range (i.e. L ¼ ,0.2 to 0.3) for these forests. Hence, large-scale spatiotemporal effects of prescribed burning on point-scale IFI and intensity (e.g. Fig. 1c, d ) were accounted for. Accordingly, the rate of unplanned fire Fu within the simulated plot was modelled as a function of the landscape-scale prescribed fire rate P, leverage L and unplanned fire rate in the absence of prescribed fire U*. Fu ¼ U n P L

ð3Þ

U* was simulated at a fixed rate of 5% per annum (pa), reflecting the typical fire cycle in the absence of prescribed burning (e.g. Price and Bradstock 2011). Prescribed fires were then simulated at a range of differing rates (P ¼ 1 to 20% pa). A minimum litter fuel load of 8 t ha1 was required for a prescribed fire to occur. Such a fuel load is typically considered to be a desirable target in fuel reduction programs in southern Australia (Gill et al. 1987). Unplanned fires, if scheduled, were allowed to burn the simulated plot at any time following prescribed fire in accordance with empirical evidence from eucalypt forests (Bradstock et al. 2010; Price and Bradstock 2011).

Wt ¼ W max 1 ekt

ð4Þ

where Wt is the fuel load (t ha1), t is time since fire (years), Wmax is the steady-state fuel load (t ha1) and k the decomposition rate. Although such models have been commonly developed for surface litter (i.e. fine fuel) in eucalypt forest, we assumed they also apply to other aboveground fuel pools, namely shrub and tree foliage, as found by Morrison et al. (1996), as well as coarse woody debris and bark (see the Supplementary material, http://www.publish.csiro.au/?act= view_file&file_id¼WF11023_AC.pdf). Responses of three fuel types were explored based on the known range of patterns of surface fine fuel accumulation in south-eastern Australian eucalypt forests (Fig. 4, Supplementary material). These were: Type I – relatively low rate of fine fuel accumulation and equilibrium load; Type II – relatively high rate of fine fuel accumulation but relatively low equilibrium load; Type III – moderate rate of fine fuel accumulation and high equilibrium load (Fig. 4). These fuel types represent a range from low to high forest productivity in response to rainfall, covering .107 ha in southern Australia. Corresponding parameters for the other aboveground pools of fuel were derived from the literature (Supplementary material). Parameters for consumption of each of the fuel pools by prescribed and unplanned fire were based on published ranges from the field (Supplementary material). Unplanned fires were assumed to consume litter and shrub fine fuels completely, as these fires usually occur at very low fuel-moisture levels (Catchpole 2002). By contrast, prescribed fires were assumed to consume 60% of both litter and shrub fine fuels (Supplementary material). Further allowance was made for fine-scale spatial heterogeneity of fuel consumption. Fuel consumption in


unplanned fires was assumed to be spatially uniform, whereas it was assumed that each prescribed fire would only burn 80% of the area of fuel within the notional 1-ha plot. This is consistent with fine-scale measures of heterogeneity of fire in temperate eucalypt forests (Ooi et al. 2006; Penman et al. 2007). The proportions of coarse woody debris, bark and overstorey foliage consumed were determined from the fire-line intensity (Supplementary material). Tree foliage was either completely consumed or not consumed at all depending on whether the estimated fire-line intensity exceeded 3000 kW m1. This pattern is typical for temperate eucalypt forests (Cheney 1981) and has been verified using remotely sensed estimates of fire severity (Bradstock et al. 2010). The estimates of available fine fuel load (i.e. surface litter plus shrub foliage) were used to calculate rate of fire spread and fire-line intensity for both prescribed and unplanned fires, using a relevant empirical fire behaviour model for eucalypt forests (Supplementary material). A representative set of meteorological data was used to characterise fire weather conditions typical of prescribed burning and major unplanned fires (Supplementary material). The model was run for a daily time-step over a 500-year period for each fuel type, with fire weather independently sampled randomly from the specified distribution in each year. All model runs were repeated 1000 times to obtain means and confidence intervals for model predictions.


Leverage 1.00 0.50 0.25 0.17

Type I

10 000

5000

0 0

Mean intensity (kW m⫺1)

634

5

10

Fuel consumption Average fuel consumption by prescribed fires was ,40% of that of unplanned fires, as indicated by trends in relative fuel consumption R (Fig. 6). R increased with increasing rate of prescribed fire treatment (Fig. 6), as predicted from Fig. 2 (i.e. fuel consumption of unplanned and prescribed fires tended to converge). The range of increase in R was relatively small and responses at different leverage values were very similar. Values and trends of R with prescribed fire rate were similar for Fuel

20

Leverage 1.00 0.50 0.25 0.17

Type II

10 000

5000

0 0

5

10

15

20

Leverage 1.00 0.50 0.25 0.17

Type III

Results Fire frequency and intensity The mean IFI (i.e. resulting from interaction of prescribed and unplanned fires) declined with increasing prescribed fire treatment rate in all fuel types (Supplementary material) in the manner predicted (Fig. 1c). Mean IFI also decreased with decreasing leverage at any given treatment rate, as predicted (Supplementary material). Estimates of mean fire intensity (prescribed plus unplanned fires combined) declined with increasing prescribed fire treatment rate in all fuel types (Fig. 5), consistent with predictions (Fig. 1d ). Fire intensity was positively related to fuel accumulation rate and equilibrium load, being highest in Fuel Type III, intermediate in Type II and lowest in Type I. Under high leverage (i.e. 1 or 0.50), mean fire intensity declined rapidly with prescribed fire treatment rate in all types at rates of treatment ,10% pa. Under low leverage (L ¼ 0.25 or 0.17), intensity also declined rapidly at rates of treatment ,10% pa, though to a lesser degree than under high leverage, but the rate of decline in intensity diminished at treatment rates .10% pa (Fig. 5).

15

10 000

5000

0 0

5

10

15

20

Prescribed burning rate P (% area pa) Fig. 5. Simulated trends in mean fire-line intensity (i.e. prescribed plus unplanned fires) under different rates of prescribed fire and different leverage values in three fuel types. Values are for a hypothetical 1-ha forest patch with a mean annual unplanned fire probability of 5% (i.e. 20-year cycle).

Types II and III, but R was slightly higher in Fuel Type I and showed a larger range of increase with prescribed fire rate, again as predicted (Fig. 2). The estimated mean rate of fuel consumption was affected by leverage and treatment rate in a similar manner across all fuel types (Fig. 7). Overall, mean rate of fuel consumption was inversely related to leverage. At high leverage (i.e. 1 or 0.5), fuel consumption rate declined with increasing treatment rate (low to moderate rates of treatment only: i.e. ,10% pa). At low leverage (i.e. 0.25 or 0.17), the rate of fuel consumption increased linearly with prescribed fire treatment, such that all rates of treatment resulted in increased rate of fuel consumption compared with the untreated scenario.



4

0.8

Type I

Type I 3

0.6

2

0.4 Leverage 1.00 0.50 0.25 0.17

0.2

0

Leverage 1.00 0.50 0.25 0.17

1

0 0

5

10

0.8

15

Leverage 1.00 0.50 0.25 0.17

Type II 0.6

5

10

15

20

20

0.4

0.2

Fuel consumption (t ha⫺1 year⫺1)

0

Relative fuel consumption R

635

4

Type II 3

2

Leverage 1.00 0.50 0.25 0.17

1

0 0

5

10

15

20

0 0

5

10

15

20

4

Type III 3

0.8

Leverage 1.00 0.50 0.25 0.17

Type III 0.6

2

Leverage 1.00 0.50 0.25 0.17

1

0.4 0 0.2

0

5

10

15

20

Prescribed burning rate P (% area pa) 0 0

5

10

15

20

Prescribed burning rate P (% area pa) Fig. 6. Simulated trends in relative fuel consumption (R) under different rates of prescribed fire and different leverage values in three fuel types. Values are for a hypothetical 1-ha forest patch with a mean annual unplanned fire probability of 5% (i.e. 20-year cycle).

Fuel consumption rate was lowest in Fuel Type I, whereas rates were similar in types II and III. The potential maximum range of increase in mean fuel consumption, at low leverage, or decrease at high leverage, was larger in Fuel Types II and III than in Type I. Discussion Potential for mitigation of carbon emissions in temperate eucalypt forests The potential for mitigation of emissions via use of prescribed fire in south-eastern Australian eucalypt forests was predicted to

Fig. 7. Simulated trends in mean annual rate of fuel consumption (total fuel) under different rates of prescribed fire and different leverage values in three fuel types. Values are for a hypothetical 1-ha forest patch with a mean annual unplanned fire probability of 5% (i.e. 20-year cycle).

be limited on the basis of the general conceptual model (Fig. 2). Estimates of fuel consumption (Fig. 7) based on patterns of fuel consumption and differences in fire intensity between planned and unplanned fires confirmed this prediction. This indicates that the conceptual model (Fig. 2) is robust and may be more widely applicable to other vegetation types. High rates of prescribed burning in temperate eucalypt forests may lead to either no change or a net increase in emissions, relative to a situation without prescribed burning, owing to the predicted trends in fuel consumption (Fig. 7). This stems from the relatively low leverage of prescribed burning that has been measured in temperate Eucalyptus-dominated forest (e.g. L ¼ ,0.25; Boer et al. 2009). Diminished fuel consumption and a net reduction in carbon emissions are only likely at

636


high leverage, well beyond the range currently measured in temperate eucalypt forests. The relatively low rates of leverage measured in eucalypt forests reflect a low rate of encounter between treated areas and subsequent wildfires. For example, Price and Bradstock (2010) found that 22.5% of prescribed burn patches were encountered within 5 years by a subsequent wildfire in eucalypt forests of the Sydney region (i.e. time period where maximum reduction in wildfire spread and severity will be achieved; Bradstock et al. 2010; Price and Bradstock 2010). Campbell et al. (2011) also concluded that the low rate of encounter of fuel treatments by wildfires in the western USA negated the potential for fuel treatments to mitigate carbon emissions. Further quantification of leverage and its underpinnings will therefore be crucial to understanding the potential for mitigation of carbon emissions from fires in a broad range of ecosystems. Mean rate of fuel consumption was related to fuel accumulation patterns (i.e. fuel types). The range of increase or decrease in fuel consumption rate achieved by prescribed burning was smallest in Fuel Type I, implying that any negative effect of management via prescribed burning (i.e. increased emissions), due to low leverage, will be smaller than in the other fuel types. Strategic prescribed burning may therefore be best used in dry forest with low rates of fuel accumulation, rather than in restricted patches of wetter, more productive forests with inherently higher and more rapidly accumulating fuel loads (i.e. Fuel Type III). This result is consistent with the prediction by Mitchell et al. (2009) that potential to mitigate emissions through fuel treatment will be higher in drier conifer forest types compared with wet coastal forests in the western USA. Although carbon emissions are unlikely to be mitigated via use of prescribed fire in temperate Australian eucalypt forests, there may be potential to alter their chemical form. The mix of GHG (e.g. CO, CO2, N2O, CH4) could differ between prescribed and unplanned fires as a function of fire intensity but more research is required to clarify this. The overall approach developed here (i.e. Fig. 2) may be used to consider the efficacy of fuel treatment in mitigating not only a wider array of GHG but also particulates arising from combustion of vegetation. Assumptions and limitations Our estimates represented average point-scale trends in fire regimes and fuel consumption within a forested landscape on level terrain. Remotely sensed estimates of fire severity in eucalypt forests (e.g. Bradstock et al. 2010) show that spatial variability in fuel consumption is strongly affected by terrain variation. Therefore, there will be substantial variation around the average trends presented here as a function of variation in terrain, vegetation and fire patterns. Biases due to these variations may be important: e.g. effects of slope and aspect variations on fire intensity and fuel consumption may be largely self-cancelling but spatial variation in fire patterns could alter probabilities of burning in ways not accounted for in our calculations. Strong effects of varied spatial patterns of fuel treatment on the spread of wildfires have been demonstrated in some theoretical simulations (e.g. Finney et al. 2007 for several North American forest types). Mitchell et al. (2009) predicted that


strategic patterns of fuel treatments could overcome adverse effects of fuel treatment on emissions for some western USA forest types. By contrast, variations in the spatial configuration of treatments did not cause substantial variations in leverage in Australian modelling studies representing long-term, largescale patterns of recurrent fire (King et al. 2008; Bradstock et al. 2012). Such differences may reflect high rates of litter fuel accumulation and propensity for long-distance ember propagation in Australian vegetation, enabling fires to readily overcome fuel breaks and reburn young fuels. Our results are therefore likely to be robust to feasible variations in spatial strategies for prescribed burning in Australian forests (i.e. leverage will not vary widely as a result of variations in spatial pattern of treatment). Differing spatial strategies will cause different portions of the landscape to be treated at differing rates. Strategic treatment of ridges and areas adjacent to roads and natural fuel breaks is commonplace in these forests. Rate of treatment of these parts of the landscape may therefore be higher than in other parts of the landscape. By contrast, we assumed that the rate of modification of unplanned fire in the simulated plot was representative of the entire landscape (i.e. U ¼ Fu) for any particular rate of treatment. Differing spatial strategies of prescribed burning derived from landscape simulations in eucalypt forests, however, had little effect on fire intensity (Bradstock et al. 2012); hence our estimates of fuel consumption (Fig. 7) may be relatively robust to this aspect of spatial variation in treatment pattern. Whereas the model deals with the known dynamics of litter fuel and disturbance by fire, we have assumed that non-litter pools in temperate forests follow a similar pattern of post-fire change. This differs from Russell-Smith et al. (2009b) who found that the mass of non-litter fuel components was relatively constant with increasing time since fire in tropical savanna and adjacent communities. Nonetheless, our assumption is reasonable given that post-fire trends in development of crown foliage are known to follow similar non-linear patterns to that of surface litter in a variety of Australian plant communities (Specht and Specht 1999). Longer-term effects of the fire regime on stand structure and productivity will also affect the potential pool of woody fuels. Although most eucalypt species resprout vigorously after fire, frequent fires may result in lower tree basal area through cumulative mortality of trees in temperate eucalypt forests (e.g. Keith 1991) and tropical savannas, particularly following high-intensity fire (e.g. Liedloff and Cook 2007). Further information on stand dynamics, consumption patterns and sensitivity of supply rates of woody material in relation to both fire intensity and frequency is therefore needed to refine estimates of fuel consumption and carbon emissions. We omitted soil carbon from fuel consumption calculations because consumption rates in fires are not well measured. In Australian sclerophyll vegetation, significant changes in soil surface temperatures as a function of heating in high-intensity fires are largely confined to the upper 5 cm (Bradstock and Auld 1995; Tozer and Auld 2006). The pool of soil carbon in this upper layer of forest soils may be of the order of 5 t ha1 (e.g. Hopmans 2003) but up to 30 t ha1 in wetter forests (Keith et al. 2009a). The range of potential soil carbon losses under differing fire regime scenarios is likely to be small and


insufficient to alter overall trends in predicted rates of fuel consumption. Much of the apparent loss of surface soil organic material after a fire is due to erosion (e.g. Doerr et al. 2006) rather than emission to the atmosphere. General consequences Estimates of Net Ecosystem Exchange (NEE) from temperate eucalypt forests (van Gorsel et al. 2008; Keith et al. 2009b) suggest that carbon is sequestered at a rate of 2 to 6 t ha1 year1 in the interval between fires. Such sequestration is offset by a rate of carbon loss from fires of ,1.0 to 1.7 t ha1 year1, using the range of mean fuel consumption rate estimated in the present study (,2 to 3.5 t ha1 year1 when L ¼ 0.17 to 0.25; Fig. 7), assuming that carbon comprises close to half the mass of fuel consumed. Thus Net Biome Productivity (NBP, where NBP ¼ NEE rate of carbon loss in fires) in these forests may be of the order of 1 to 5 t ha1 year1. High rates of burning from both prescribed (Fig. 7) and unplanned fires would tend to diminish NBP and sequestration capacity in these forests. Simulations of carbon storage in some eucalypt forests under climate change and resultant elevated fire activity are consistent with this prediction (King et al. 2011). Fire weather and fuel loads may change under a future climate (Williams et al. 2009; Bradstock 2010). A warmer future climate may increase the average frequency, intensity and resultant fuel consumption of fires in forests of southeastern Australia (Williams et al. 2009; Bradstock 2010). This could diminish the efficacy of prescribed burning (i.e. reduced leverage) and therefore further reduce any potential for mitigation of emissions (Fig. 2). Higher rates of unplanned fire, as anticipated under climate change, may increase overall fire intensity, fuel consumption and carbon emissions and lower carbon storage in temperate eucalypt forests (King et al. 2011). By contrast, increasing dryness under global warming, as well as elevated fire activity, may lead to a diminution of productivity, with a resulting decline in fuel accumulation rate and maximum load in eucalypt forests (Bradstock 2010). Comparisons among the fuel types indicate that this may tend to diminish fuel consumption and emissions: i.e. mean fuel consumption rate is lower in Type I cf. Types II and III (Fig. 7). Prognoses for climate change may be uncertain, but an initial increase in fire activity and rate of carbon loss in these forests may facilitate the transition to a lower-productivity and sequestration state in the longer term. The potential for mitigation of emissions in temperate forests contrasts with the situation in tropical savannas of northern Australia. Although temperate forests and tropical savannas share a eucalypt-dominated overstorey, their fire regimes are inherently different owing to contrasts in climate and fuel availability (Bradstock 2010). These fundamental differences appear to substantially affect the efficacy of prescribed burning (i.e. L ,, 1 in forests; L $ 1 savannas). Potential for mitigation of emissions via active fire management is therefore different in these two biomes. Higher efficacy (leverage) of prescribed fire in savannas may yield higher potential for use of prescribed fire to reduce emissions across this extensive biome. This, coupled with the vast areas of savannas (,2000 103 km2) and the annual extent of fires (average ,340 103 km2 pa), results in


637

significant scope for mitigation of emissions via land management in northern Australia (Russell-Smith et al. 2009a, 2009b). Although southern temperate forests occupy considerably less area (,2.5 105 km2) than the savannas of northern Australia, carbon storage is higher (e.g. pools per unit area in south-eastern forests are among the highest in the world; Keith et al. 2009a). Lower leverage in the temperate Australian forests is predicted to negate any potential for significant mitigation of emissions based on the detailed scenarios explored here. This contrasts with optimistic assessments of the potential for prescribed fire to mitigate emissions in temperate forests of North America and Europe (Narayan et al. 2007; Hurteau et al. 2008; Hurteau and North 2009) but is consistent with the scenarios modelled by Campbell et al. (2011) for the western USA. In Australia, further research needs to target the vast arid woodlands and shrublands of the interior. Continental-scale emissions will be strongly influenced by fire activity in these highly flammable deserts. The effectiveness of fuel treatment can be expected to vary across many other ecosystems as a function of inherent variation in fire regimes. The conceptual model (Fig. 2) may have wide applicability for prediction of emissions mitigation potential if information on leverage is available. Quantification of the efficacy of treatment of fuels in altering the incidence, size and intensity of wildfires through techniques such as prescribed burning will be a key priority for understanding the global potential to mitigate emissions from landscape fires. Acknowledgements This research was carried out by Working Group Number 53 (‘Fire and Carbon Budgets’; http://www.vegfunction.net/wg/53/53_Fire_Carbon_ Budgets.htm, accessed 8 February 2011) of the Australia–New Zealand Network for Vegetation Function. Assistance was also kindly provided by the Bushfire Cooperative Research Centre, the CSIRO Climate Adaptation Flagship and the CSIRO Sustainable Agriculture Flagship. We thank Brett Murphy, Alan York, Craig Fensham, Roger Ottmar and several anonymous reviewers for their critical comments on the manuscript.

References Boer MM, Sadler RJ, Wittkuhn R, McCaw L, Grierson PF (2009) Longterm impacts of prescribed burning on regional extent and incidence of wildfires – evidence from 50 years of active fire management in SW Australian forests. Forest Ecology and Management 259, 132–142. doi:10.1016/J.FORECO.2009.10.005 Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, D’Antonio CM, DeFries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice C, Roos CI, Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009) Fire in the Earth system. Science 324, 481–484. doi:10.1126/SCIENCE. 1163886 Bradstock RA (2010) A biogeographic model of fire regimes in Australia: contemporary and future implications. Global Ecology and Biogeography 19, 145–158. doi:10.1111/J.1466-8238.2009.00512.X Bradstock RA, Auld TD (1995) Soil temperatures during experimental bushfires in relation to fire intensity: consequences for legume germination and fire-management in south-eastern Australia. Journal of Applied Ecology 32, 76–84. doi:10.2307/2404417 Bradstock RA, Williams RJ (2009) Can Australian fire regimes be managed for carbon benefits? New Phytologist 183, 931–934. doi:10.1111/J.14698137.2009.02958.X

638


Bradstock RA, Hammill K, Collins L, Price O (2010) Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia. Landscape Ecology 25, 607–619. doi:10.1007/ S10980-009-9443-8 Bradstock RA, Cary GJ, Davies I, Lindenmayer DB, Price OF, Williams RJ (2012) Wildfires, fuel treatment and risk mitigation in Australian eucalypt forests: insights from landscape-scale simulation. Journal of Environmental Management 105, 66–75. doi:10.1016/ J.JENVMAN.2012.03.050 Campbell JL, Harmon ME, Mitchell SR (2011) Can fuel-reduction treatments really increase forest carbon storage in the western US by reducing future fire emissions? Frontiers in Ecology and the Environment doi:10.1890/110057 Catchpole W (2002) Fire properties and burn patterns in heterogeneous landscapes. In ‘Flammable Australia: the Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, AM Gill) pp. 50–75. (Cambridge University Press: Cambridge, UK) Cheney NP (1981) Fire behaviour. In ‘Fire and the Australian Biota’. (Eds AM Gill, RH Groves, IR Noble) pp. 151–176. (Australian Academy of Science: Canberra, ACT) Doerr SH, Shakesby RA, Blake WH, Chafer CJ, Humphreys GS, Wallbrink PJ (2006) Effects of differing wildfire severities on soil wettability and implications for hydrological responses. Journal of Hydrology 319, 295–311. doi:10.1016/J.JHYDROL.2005.06.038 Fernandes PM, Bothello HS (2003) A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire 12, 117–128. doi:10.1071/WF02042 Finney MA, Seli RC, McHugh CW, Ager AA, Bahro B, Agee JK (2007) Simulation of long-term landscape-level fuel treatment effects on large wildfires. International Journal of Wildland Fire 16, 712–727. doi:10.1071/WF06064 Gill AM, Catling PC (2002) Fire regimes and biodiversity of forested landscapes of southern Australia. In ‘Flammable Australia: the Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, AM Gill) pp. 351–369. (Cambridge University Press: Cambridge, UK) Gill AM, Christian KR, Moore PHR, Forrester RI (1987) Bushfire incidence, fire hazard and fuel reduction burning. Australian Journal of Ecology 12, 299–306. doi:10.1111/J.1442-9993.1987.TB00950.X Gill AM, Ryan PG, Moore PHR, Gibson M (2000) Fire regimes of World Heritage Kakadu National Park, Australia. Austral Ecology 25, 616–625. Hopmans P (2003) Effects of repeated low-intensity fire on carbon, nitrogen and phosphorus in the soils of a mixed-eucalypt foothill forest in southeastern Australia. Research Report Number 60. (Victorian Department of Sustainability and Environment: Melbourne) Hurteau MD, North M (2009) Fuel treatment effects on tree-based forest carbon storage and emissions under modeled wildfire scenarios. Frontiers in Ecology and the Environment 7, 409–414. doi:10.1890/ 080049 Hurteau MD, Koch GW, Huntgate BA (2008) Carbon protection and fire risk reduction: toward a full accounting of forest carbon offsets. Frontiers in Ecology and the Environment 6, 493–498. doi:10.1890/ 070187 Keeley JE, Zedler PH (2009) Large, high-intensity fire events in southern California shrublands: debunking the fine-grain age patch model. Ecological Applications 19, 69–94. doi:10.1890/08-0281.1 Keith H (1991) Effects of fire and fertilisation on nitrogen cycling and tree growth in a subalpine eucalypt forest. PhD dissertation, Australian National University, Canberra. Keith H, Mackey BG, Lindenmayer DB (2009a) Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proceedings of the National Academy of Sciences of the United States of America 106, 11 635–11 640.


Keith H, Leuning RL, Jacobsen KL, Cleugh HA, van Gorsel E, Raison RJ, Medlyn BE, Winter A, Keitel C (2009b) Multiple measurements constrain estimates of net carbon exchange by a Eucalypt forest. Agricultural and Forest Meteorology 149, 535–558. doi:10.1016/ J.AGRFORMET.2008.10.002 King KJ, Bradstock RA, Cary G, Chapman C, Marsden-Smedley J (2008) An investigation into the relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia. International Journal of Wildland Fire 17, 421–430. doi:10.1071/WF07052 King KJ, de Ligt RM, Cary GJ (2011) Fire and carbon dynamics under climate change in south-eastern Australia: insights from FullCAM and FIRESCAPE modelling. International Journal of Wildland Fire 20, 563–577. Krivtsov V, Vigy C, Legga T, Curtc E, Rigolot I, Lecomteb M, Jappiotc C, Lampin-Maillet PF, Pezzatti GB (2009) Fuel modelling in terrestrial ecosystems: an overview in the context of the development of an objectorientated database for wildfire analysis. Ecological Modelling 220, 2915–2926. doi:10.1016/J.ECOLMODEL.2009.08.019 Liedloff AC, Cook GD (2007) Modelling the effects of rainfall variability and fire on tree populations in an Australian tropical savanna with the FLAMES simulation model. Ecological Modelling 201, 269–282. doi:10.1016/J.ECOLMODEL.2006.09.013 Loehle C (2004) Applying landscape principles to fire hazard reduction. Forest Ecology and Management 198, 261–267. doi:10.1016/ J.FORECO.2004.04.010 Mitchell SR, Harmon ME, O’Connell KEB (2009) Forest fuel reduction alters fire severity and long-term carbon storage in three Pacific Northwest ecosystems. Ecological Applications 19, 643–655. doi:10.1890/08-0501.1 Morrison DA, Buckney RT, Bewick BJ, Cary CJ (1996) Conservation conflicts over burning bush in south-eastern Australia. Biological Conservation 76, 167–175. doi:10.1016/0006-3207(95)00098-4 Narayan C, Fernandes PM, van Brusselen J, Schuck A (2007) Potential for CO2 emissions mitigation in Europe through prescribed burning in the context of the Kyoto Protocol. Forest Ecology and Management 251, 164–173. doi:10.1016/J.FORECO.2007.06.042 Ooi MKJ, Whelan RJ, Auld TD (2006) Persistence of obligate-seeding species at the population scale: effects of fire intensity, fire patchiness and long fire-free intervals. International Journal of Wildland Fire 15, 261–269. doi:10.1071/WF05024 Penman TD, Kavanagh RP, Binns DL, Melick DR (2007) Patchiness of prescribed burns in dry sclerophyll forests in south-eastern Australia. Forest Ecology and Management 252, 24–32. doi:10.1016/J.FORECO. 2007.06.004 Price OF, Bradstock RA (2010) The effect of fuel age on the spread of fire in sclerophyll forest in the Sydney region of Australia. International Journal of Wildland Fire 19, 35–45. doi:10.1071/WF08167 Price OF, Bradstock RA (2011) The influence of weather and fuel management on the annual extent of unplanned fires in the Sydney region of Australia. International Journal of Wildland Fire 20, 142–151. Raison RJ, Woods PV, Khanna PK (1983) Dynamics of fine fuels in recurrently burnt eucalypt forests. Australian Forestry 46, 294–302. Russell-Smith J, Whitehead PJ, Cooke P (2009a) ‘Culture, Ecology and Economy of Fire Management in North Australian Savannas: Rekindling the Wurrk Tradition.’ (CSIRO Publishing: Melbourne) Russell-Smith J, Murphy BP, Meyer CP, Cook GD, Maier S, Edwards AC, Schatz J, Brocklehurst P (2009b) Improving estimates of savanna burning emissions for greenhouse accounting in northern Australia: limitations, challenges, applications. International Journal of Wildland Fire 18, 1–18. doi:10.1071/WF08009 Specht RL, Specht A (1999) ‘Australian Plant Communities: Dynamics of Structure, Growth and Biodiversity.’ (Oxford University Press: Melbourne)



Tozer MG, Auld TD (2006) Soil heating and germination: investigations using leaf scorch on graminoids and experimental seed burial. International Journal of Wildland Fire 15, 509–516. doi:10.1071/WF06016 van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Kasibhatla PS, Arellano AF Jr (2006) Inter-annual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics 6, 3423–3441. doi:10.5194/ACP-6-3423-2006 van Gorsel E, Leuning R, Cleugh HA, Keith H, Kirschbaum MUF, Suni T (2008) Application of an alternative method to derive reliable estimates of night-time respiration from eddy covariance measurements in moderately complex topography. Agricultural and Forest Meteorology 148, 1174–1180. doi:10.1016/J.AGRFORMET.2008.01.015

639

Wiedinmyer C, Hurteau MD (2010) Prescribed fire as a means of reducing forest carbon emissions in the western United States. Environmental Science & Technology 44, 1926–1932. doi:10.1021/ES902455E Williams RJ, Bradstock RA, Cary GJ, Enright NJ, Gill AM, Liedloff AC, Lucas C, Whelan RJ, Andersen AN, Bowman DMJS, Clarke PJ, Cook GD, Hennessy KJ, York A (2009) Interactions between climate change, fire regimes and biodiversity in Australia – a preliminary assessment. Report to the Department of Climate Change and Department of Environment, Water, Heritage and the Arts. (Canberra, ACT)

www.publish.csiro.au/journals/ijwf