The Drivers of Effectiveness of Prescribed Fire Treatment

Abstract

Prescribed burning for fuel reduction is a major strategy for reducing the risk from unplanned fire. Although there are theoretical studies suggesting that prescribed fire has a strong negative influence on the subsequent area of unplanned fire (so-called leverage), many empirical studies find a more modest influence. Here, I develop a series of simulations to explore the landscape drivers of leverage. Leverage declines with treatment level in a nonlinear, "decay" relationship, implying diminishing effectiveness. The spatial configuration of the prescribed fire treatment has a major effect: long linear (gridded) barriers are far more effective than patch barriers, but gaps in the grid lead to large reductions in leverage. However, the extent of unplanned fires in the landscape has the largest influence such that a landscape with 3% annual extent has only one-fifth of the leverage of a landscape with 28%. Leverage decreases with the probability of spread, suggesting that treatment is less effective when fire weather is severe. For gridded designs, leverage increases with the size of individual fires, but this is not the case for patch designs. These results agree well with recent empirical studies finding that prescribed burning has only a modest effect on subsequent unplanned fire in many biomes. They also help to explain why those empirical studies report lower effectiveness than many simulation studies. In practice, leverage values >1 (replacement of unplanned with planned fire) are hard to achieve. The drivers of effectiveness of prescribed fire treatment AbstractPrescribed burning for fuel reduction is a major strategy for reducing the risk from unplanned fire. While there are theoretical studies suggesting that prescribed fire has a strong negative influence on the subsequent area of unplanned fire (so called leverage), many empirical studies find a more modest influence. Here, I develop a series of simulations to explore the landscape drivers of leverage. Leverage declines with treatment level in a non-linear, 'decay' relationship, implying diminishing effectiveness. The spatial configuration of the prescribed fire treatment has a major effect: long linear (gridded) barriers are far more effective than patch barriers, but gaps in the grid lead to large reduction in leverage. However, the extent of unplanned fires in the landscape has the largest influence such that a landscape with 3% annual extent has only one fifth of the leverage of a landscape with 28%. Leverage decreases with the probability of spread, suggesting that treatment is less effective when fireweather is severe. For gridded designs, leverage increases with the size of individual fires, but this is not the case for patch designs. These results agree well with recent empirical studies that find prescribed burning has only a modest effect on subsequent unplanned fire in many biomes. They also help to explain why those empirical studies report lower effectiveness than many simulation studies. In practice, leverage values above 1 (repla...

Is there an inherent conflict in managing fire for people and conservation?

“…Leverage varies according to the annual extent of wildfire, extent of fuel treatment and the rate at which fuel loads recover (Price 2012). Leverage has been analysed on individual burns (Loehle 2004) and across landscapes (Boer et al 2009;Price et al 2012;Price et al 2015a).…”

Empirical Support for the Use of Prescribed Burning as a Fuel Treatment

“…At best, as indicated by leverage studies, PB will replace wildfire area. Given the relatively low return for effort of PB, it is crucial to carefully consider its spatial patterns [22], namely by using adequate optimization methods [76]. Insufficient resources and other constraints can easily preclude treatment of the required fraction of the landscape for PB to have a tangible effect on wildfire extent.…”

“…This review updates the state-of-the-art on PB effectiveness but excludes studies that have used semi-empirical fire modelling systems, namely because of incorrect model linkages and underprediction bias [19], and output susceptibility to the assumptions used [20]. PB effectiveness inferred from fire modelling can oppose experimental results [21] and is optimistic in comparison with the findings of landscape scale empirical analysis [22]. We also ignored studies of post-treatment dynamics of fuel loading and structure that lacked companion fire behaviour data.…”

Prescribed burning in Italy: issues, advances and challenges

“…As regard fire hazard reduction, the PB effectiveness in abating the overall burnt area (i.e., leverage) is proportional to the wildfire area extent of a specific territory (Price 2012). When wildfire incidence on the overall vegetation area is low, most of the treated areas are not encountered by a fire and so cannot be effective.…”

“…2) seems to not justify a PB strategy at the national level. However, fire management is set by Regional administrations ) which identify local priority areas where wildfire incidence is high and where PB, implemented in a strategic spatial design (Price 2012), could be effective. Moreover, the landscape fragmentation and relatively less severe fire weather which characterize the Italian territory may increase PB effectiveness, as reported for other southern European countries (Vilén & Fernandes 2011).…”