Modeling wildfire activity is crucial for informing science‐based risk management and understanding the spatiotemporal dynamics of fire‐prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability, and provide insights into specific events. Here, we develop Firelihood, a two‐component, Bayesian, hierarchically structured, probabilistic model of daily fire activity, which is modeled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space‐time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the stochastic partial differential equation (SPDE) approach. The size model is based on piecewise‐estimated Pareto and generalized Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and forest area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1,000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1 ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10–100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burned areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1 ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burned areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.
Integrated simulation models are commonly used to provide insight on the complex functioning of social-ecological systems, often drawing on earlier tools with a narrower focus. Forest sector models (FSM) encompass a set of simulation models originally developed to forecast economic developments in timber markets but now commonly used to analyse climate and environmental policy. In this paper, we document and investigate this evolution through the prism of the inclusion of several non-timber objectives into FSM. We perform a systematic, quantitative survey of the literature followed by a more in-depth narrative review. Results show that a majority of papers in FSM research today focuses on non-timber objectives related to climate change mitigation, namely carbon sequestration and bioenergy production. Habitat conservation, deforestation and the mitigation of disturbances are secondary foci, while aspects such as forest recreation and many regulation services are absent. Non-timber objectives closest to the original targets of FSM, as well as those for which economic values are easier to estimate, have been more deeply integrated to the models, entering the objective function as decision variables. Others objectives are usually modelled as constraints and only considered through their negative economic impacts on the forest sector. Current limits to a deeper inclusion of non-timber objectives include the models' ability to represent local environmental conditions as well as the formulation of the optimisation problem as a maximisation of economic welfare. Recent research has turned towards the use of model couplings and the development of models at the local scale to overcome these limitations. Challenges for future research comprise extensions to other non-timber objectives, especially cultural services, as well as model calibration at lower spatial scales.
Background. Identifying if and how climatic and non-climatic factors drive local changes in fire regimes is, as in many other human-dominated landscapes, challenging in south-eastern France where both heterogeneous spatial patterns and complex fire trends are observed. Aim. We sought to identify the factors driving the spatial-temporal patterns of fire activity in southeastern France. Methods. We incorporated several non-climatic variables into the probabilistic Firelihood model of fire activity and implemented an enhanced spatio-temporal component to quantitatively assess remaining unexplained variations in fire activity. Key results. Several non-climatic drivers (i.e. orography, land cover and human activities) contributed as much as fire-weather to the distribution of fire occurrence (>1 ha) but less to larger fires (>10, 100 and 1000 ha). Over the past decades, increased fire-weather induced a strong increase in wildfire probabilities, which was actually observed on the western part of the region but not so in the east and Corsican Island, most likely due to reinforced suppression policies. Conclusions. While spatial patterns in fire activity are driven by land-use and land-cover factors, temporal patterns were mostly driven by changes in fire-weather and unexplained effects potentially related to suppression policies but with large differences between regions.
We explore the implications of managing forests for the dual purpose of sequestering carbon and producing timber, using a model of the forest sector that includes a Hartman-based representation of forest owners’ behaviour as well as heterogeneity in environmental conditions. We focus on France, where recent policies aim at increasing the carbon sink and where the diversity of forests makes an analysis of spatial dynamics relevant, and we use recent estimates of the shadow price of carbon consistent with the country’s climate commitments. Results suggest that forests may sequester up to 550 MtCO2eq by 2100, driven by changes in harvest levels and species choice, whilst rotation lengths increase overall. A spatial analysis reveals a high spatial variability for these trends, highlighting the importance of considering the local context. Changes in investment patterns affect the spatial distribution of forest cover types: by the end of the century, a majority of regions comprise a larger share of older, multiple-species and mixed-structure forests. Whilst such an evolution may present benefits in terms of biodiversity, ecosystem services provision and resilience, it raises questions regarding the adequacy of such developments with current forest policy, which also aims at increasing harvest levels. An overall mitigation strategy for the forest sector would likely include incentives to energy and material substitution in downstream industries, which we did not consider and may interact with sequestration incentives.
Under the influence of climate change, wildfire regimes are expected to intensify and expand to new areas, increasing threats to natural and socioeconomic assets. We explore the environmental and economic implications for the forest sector of climate‐induced changes in wildfire regimes. To retain genericity while considering local determinants, we focus on the regional level and take Mediterranean France as an example. Coupling a bioeconomic forest sector model and a model of wildfire activity, we perform spatially explicit simulations under various levels of radiative forcing. By using a probabilistic framework, we also assess the propagation of several sources of uncertainty to the forest sector, considering both climate‐induced uncertainty and the intrinsic stochasticity of the fire process. By the end of the century, summer burned areas increase by up to 55%, causing moderate losses of merchantable timber and forest carbon stocks, with cascading impacts for industrial activities and climate mitigation in the forest sector. Implications for industries remain limited, but we observe price increases, especially for softwoods, as well as spatially differentiated changes in producer welfare. Inter‐annual fluctuations explain most of uncertainty in wildfire activity, but their impacts on the forest sector are quickly dampened. Over time, owing to the cumulative nature of wildfire impacts on forest resources, uncertainty related to climate warming, climate models’ response and stochasticity intrinsic to the wildfire phenomenon strongly increase in relative importance. Results reassert the need to consider multiple futures in prospective assessments, including uncertainty inherent to natural processes, often omitted in large‐scale economic assessments.
Modelling wildfire activity is crucial for informing science-based risk management and understanding fire-prone ecosystem functioning worldwide. Models also help to disentangle the relative roles of different factors, to understand wildfire predictability or to provide insights into specific events. • Here, we develop a two-component Bayesian hierarchically-structured probabilistic model of daily fire activity, which are modelled as the outcome of a marked point process in which individual fires are the points (occurrence component) and the fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the Stochastic Partial Differential Equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and Generalized-Pareto distributions, also adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Seasonal and spatial residuals as well as a post-2003 effect are included to improve the consistency of the relationship between climate and fire occurrence, in accordance with parsimonious criteria. • A set of 1000 simulations of the posterior model of fire activity is evaluated at various temporal and spatial scales in Mediterranean France. The number of escaped fires (≥1ha) across the region can be coarsely reproduced at the daily scale, and is more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) can be predicted as well, but the accuracy decays with size, as the model uncertainty increases with event rareness. Local predictions of fire numbers or burnt areas likewise require a longer aggregation period to maintain model accuracy.• Regarding the year 2003 -which was characterized by an extreme burnt area in France associated with a heat wave-, the estimation of the number of escaped fires was consistent with observations, but the model systematically underrepresents larger fires and burnt areas, which suggests that the FWI does not consistently rate the danger of large fire occurrence during heat waves. • Our study sheds new light on the stochastic processes underlying fire hazard, and is promising for predicting and projecting future fire hazard in the context of climate change.summer following a prolonged drought (Trigo et al. 2005). We finally discuss the strength and weaknesses of the current model and its potential applications for wildfire-related research avenues and the improvement of operational fire suppression and management. METHODS Data and site descriptionStudy site and fire activity. The study area consists of 15 NUTS3-level French administrative units located in southeastern France (Fig. 1A, 75,560 km 2 ), which concentrate the vast majority of burnt area during the summer season in France. The climate of this area is mostly Mediterranean, characterized by cool and moist winters and hot and dry summers, but exhibits strong variations with orography,...
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