Open Access
March 2023 Spatiotemporal wildfire modeling through point processes with moderate and extreme marks
Jonathan Koh, François Pimont, Jean-Luc Dupuy, Thomas Opitz
Author Affiliations +
Ann. Appl. Stat. 17(1): 560-582 (March 2023). DOI: 10.1214/22-AOAS1642


Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. Here, we develop a joint model for the occurrence intensity and the wildfire size distribution, by combining extreme-value theory and point processes within a novel Bayesian hierarchical model, and use it to study daily summer wildfire data for the French Mediterranean basin during 1995–2018. The occurrence component models wildfire ignitions as a spatiotemporal log-Gaussian Cox process. Burnt areas are numerical marks attached to points and are considered as extreme if they exceed a high threshold. The size component is a two-component mixture varying in space and time that jointly models moderate and extreme fires. We capture nonlinear influence of covariates (Fire Weather Index, forest cover) through component-specific smooth functions which may vary with season. We propose estimating shared random effects between model components to reveal and interpret common drivers of different aspects of wildfire activity. This increases parsimony and reduces estimation uncertainty, giving better predictions. Specific stratified subsampling of zero counts is implemented to cope with large observation vectors. We compare and validate models through predictive scores and visual diagnostics. Our methodology provides a holistic approach to explaining and predicting the drivers of wildfire activity and associated uncertainties.

Funding Statement

The first author gratefully acknowledges the Swiss National Science Foundation (project 200021_178824) and the Oeschger Centre for Climate Change Research, University of Bern.


The authors would like to thank the Editor and two anonymous reviewers for their valuable comments, and Anthony Davison for helpful insights and discussions.


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Jonathan Koh. François Pimont. Jean-Luc Dupuy. Thomas Opitz. "Spatiotemporal wildfire modeling through point processes with moderate and extreme marks." Ann. Appl. Stat. 17 (1) 560 - 582, March 2023.


Received: 1 July 2021; Revised: 1 January 2022; Published: March 2023
First available in Project Euclid: 24 January 2023

MathSciNet: MR4539044
zbMATH: 07656989
Digital Object Identifier: 10.1214/22-AOAS1642

Keywords: Bayesian hierarchical model , Cox process , extreme-value theory , forest fires , shared random effects

Rights: Copyright © 2023 Institute of Mathematical Statistics

Vol.17 • No. 1 • March 2023
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