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Activity Number: 380 - Advances in Bayesian Extreme Value Analysis
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #322675
Title: Spatiotemporal Wildfire Modeling Through Point Processes with Moderate and Extreme Marks
Author(s): Jonathan Koh* and Fran├žois Pimont and Jean-Luc Dupuy and Thomas Opitz
Companies: University of Bern and INRAe and INRAe and INRAe
Keywords: Bayesian hierarchical model; Cox process; Extreme-value theory; Forest fires; Shared random effects
Abstract:

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 non-linear 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.


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