Mapping future fire probability under climate change: Does vegetation matter?

Abstract

Understanding where and how fire patterns may change is critical for management and policy decision-making. To map future fire patterns, statistical correlative models are typically developed, which associate observed fire locations with recent climate maps, and are then applied to maps of future climate projections. A potential source of uncertainty is the common omission of static or dynamic vegetation as predictor variables. We therefore assessed the sensitivity of future fire projections to different combinations of vegetation maps used as explanatory variables in a statistically based fire modeling framework. We compared models without vegetation to models that incorporated static vegetation maps and that included output from a dynamic vegetation model that imposed three scenarios of fire and one scenario of land use change. We mapped projected future probability of all and large fires (> = 40 ha) under two climate scenarios in a heterogeneous study area spanning a large elevational gradient in the Sierra Nevada, California, USA. Results showed high model sensitivity to the treatment of vegetation as a predictor variable, particularly for models of large fire probability and for models accounting for wildfire effects on vegetation, which lowered future fire probability. Some scenarios resulted in opposite directional trends in the extent and probability of future fire, which could have serious implications for policy and management resource allocation. Model sensitivity resulted from high relative importance of vegetation variables in the baseline models and from large predicted changes in vegetation, particularly when simulating wildfire. Although statistical fire models often omit vegetation due to uncertainty, model sensitivity demonstrated here suggests a need to account for that uncertainty. Coupling statistical and processed based models may be a promising approach to reflect a more plausible range of scenarios.

Global rangeland production systems and livelihoods at threat under climate change and variability

“…Rangeland community vulnerability depends not only on climate impacts on ecosystems processes but also on the ability of these communities to change in response to or cope with stressors, i.e. adaptive capacity (Gallopín 2006, Stafford Smith et al 2011, Marshall 2015. A particular attribute of rangeland systems compared with other food production systems is that they are mostly located in remote areas with few people, whom tend to have limited adaptive capacity (Thomas and Twyman 2005, Godber and Wall 2014, Marshall 2015.…”

Fire regime and ecosystem responses: adaptive forest management in a changing world (Part 2)

“…Wildfires are a key ecosystem process, but fire regimes are changing around the world, mainly due to land-use or land-cover change (LULCC) and changing climate. Influence of LULCC is especially strong in Mediterranean areas (Pausas and Keeley 2014;Syphard et al 2018). Larger and more severe fires reduce suppression options and may have increasingly adverse effects on ecosystems, depending on fire recurrence and ecosystem resistance and resilience (Moreira et al 2012).…”

Correspondence: Uncertainty in Climate-Vegetation Feedbacks on Fire Regimes Challenges Reliable Long-Term Projections of Burnt Area from Correlative Models

“…Under this contrasting view, we could expect in fact lower BA projections compared to what is expected from nonstationary models. It is worth noticing that recent sensibility analyses comparing correlative and mechanistic models carried out in Western USA actually support the view that under particular conditions, and when accounting for negative climate-vegetation feedbacks, BA projections can in fact decrease in comparison with current figures [2]. Overall, assumptions on specific vegetation responses to climate productivity changes for which mechanisms are not yet fully understood are likely to induce contradictory effects on future estimate of BAs.…”

“…Recent studies have explored the use of simple correlative models to project changes in future burnt areas (BAs) around the globe [1,2]. In a recent application of this approach, Turco et al (2018) [3] published an elegant work in which they apply a simple correlative model to project an increase in future burnt areas (BAs) for the Mediterranean region.…”

“…In their study, the authors relate BAs to regional estimates of cumulative drought surrogates derived from evapotranspiration indices, and later they use this relationship to infer changes derived from future climate data. However, estimates of future burnt areas suffer from the critical shortcomings that positive and negative feedbacks of climate change on vegetation (i.e., climate may actually reduce vegetation growth and eventually decrease fire danger) are not included [2]. To overcome this problem, a way around this shortcoming is proposed by using regional variability in the BA-drought relationship (what they call non-stationary models) to account for future changes in fire regimes derived from climate effects on vegetation.…”