Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

Koh, Jonathan; Pimont, François; Dupuy, Jean-Luc; Opitz, Thomas (2023). Spatiotemporal wildfire modeling through point processes with moderate and extreme marks. The annals of applied statistics, 17(1), pp. 560-582. Institute of Mathematical Statistics 10.1214/22-AOAS1642

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science
10 Strategic Research Centers > Oeschger Centre for Climate Change Research (OCCR)

UniBE Contributor:

Koh Boon Han, Jonathan

Subjects:

300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 510 Mathematics

ISSN:

1932-6157

Publisher:

Institute of Mathematical Statistics

Language:

English

Submitter:

Jonathan Koh Boon Han

Date Deposited:

20 Feb 2023 10:12

Last Modified:

20 Feb 2023 23:27

Publisher DOI:

10.1214/22-AOAS1642

BORIS DOI:

10.48350/178945

URI:

https://boris.unibe.ch/id/eprint/178945

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