Statistical Models of Key Components of Wildfire Risk

Xi, Dexen D. Z.; Taylor, Stephen; Woolford, Douglas G.; Dean, C. B.

doi:10.1146/annurev-statistics-031017-100450

Cited by 57 publications

(58 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AUCs were computed for both the 1995-2014 (training) and 2015-2018 (validation) periods. Second, we evaluated model performance by comparing simulations with historical observations aggregated on various temporal and spatial scales (Xi et al 2019). These evaluations were carried out from 1000 model simulations of fire occurrence per voxel, which were sampled as a Poisson process according to draws from the posterior predictive distributions of the intensity in occurrence model.…”

Section: Fire Size Componentmentioning

confidence: 99%

“…Third, the size model failed to reproduce the extreme year (2003), although the occurrence model accurately simulated the weekly number of escaped fires during this period. Overall, one very challenging aspect of fire activity modelling is the evolution of the fire-weather relationship over time, which is explained, amongst other causes, by changes in agricultural practices (including land abandonment), in LU-LC factors, in suppression means, but also in detection efficiency (Xi et al 2019). In the present study, we used a crude approach to model the very abrupt shift in the relationship occurring between 2003 and 2005 (using a single global, not spatially resolved regression coefficient), resulting in a reduction by more than two of the escaped fire density given the same FWI value.…”

Section: Model Limitationsmentioning

confidence: 99%

“…Moreover, such probabilistic approach, as well as those including time-varying spatial effects can help to identify changes in fire-weather relationship over time, related to operational aspects. This includes detection efficiency variation, as well as the evolution in strategies, tools and techniques for fire suppression (Xi et al 2019).…”

Section: Model Applications and Perspectivesmentioning

confidence: 99%

“…In this context, the design and application of new policies need reinforced land management and planning, while fire suppression must continue to play a key role in the protection of human lives and assets. For planning purposes, managers and policy makers need to anticipate future scenarios-based fire regimes, while for preparedness and response actions, fire managers need to be informed on a daily or weekly basis of the expected number, size, duration and spread rate of fires (Taylor et al 2013;Xi et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Second, the evaluation of the underlying model performance in a probabilistic framework is not straightforward. Indeed, it requires checking the goodness-of-fit and model parsimony in the model-building framework through various approaches including information criteria, comparisons to observations aggregated on various time and spatial scales and external validation using hold-out data (Xi et al 2019). Third, early probabilistic approaches combined models of occurrence and exceedance of fire size above high fixed thresholds, but they did not simulate the size of fire events.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Prediction of regional wildfire activity with a probabilistic Bayesian framework

Pimont

Fargeon

Opitz

et al. 2020

Preprint

View full text Add to dashboard Cite

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,...

show abstract

Section: Fire Size Componentmentioning

confidence: 99%

Section: Model Limitationsmentioning

confidence: 99%

Section: Model Applications and Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of regional wildfire activity with a probabilistic Bayesian framework

Pimont

Fargeon

Opitz

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Canadian contributions to environmetrics

et al. 2022

View full text Add to dashboard Cite

This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada.Résumé: Cet article souligne l'ampleur de la collaboration entre les scientifiques canadiens dans le domaine de la statistique et présente un survol des travaux réalisés par des spécialistes du sujet sur un ensemble de thèmes liés aux sciences de l'environnement. Les auteurs relatent différents moyens par lesquels cette collaboration s'est incarnée et a permis aux Canadiennes et Canadiens de contribuer à l'environnemétrie et à divers domaines connexes, notamment les sciences du climat, la foresterie et les pêches, dont l'importance est capitale pour la gestion des ressources naturelles du pays.

show abstract

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

Pimont

Fargeon

Opitz

et al. 2021

Ecological Applications

View full text Add to dashboard Cite

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.

show abstract

Statistical Models of Key Components of Wildfire Risk

Cited by 57 publications

References 102 publications

Prediction of regional wildfire activity with a probabilistic Bayesian framework

Prediction of regional wildfire activity with a probabilistic Bayesian framework

Canadian contributions to environmetrics

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

Contact Info

Product

Resources

About