Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. We here 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 leads to increased parsimony and reduced estimation uncertainty with 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.
翻译:在1995-2018年期间,对导致温和和大野火的条件进行精确的零星时空建模,使人们更好地了解导致易燃生态系统的机制,并改进风险管理。我们在此开发一个发生强度和野火规模分布的联合模型,将极端价值理论和点数分布结合在新颖的巴伊西亚等级模型中,并用它来研究法国地中海盆地1995-2018年期间的每日夏季野火数据。发生部分模型野火点火作为温和时空日对日对日对日对日对日对日对地对地对地对地对地。燃烧区域是连接点的数值标记,如果临界值超过高阈值,则被认为是极端的。大小部分是一个在空间和时间上不同、共同模拟中度和极端火灾的两种成分混合体。我们通过特定部件的顺畅功能收集共变数(纤维天气指数、森林覆盖)的非线性影响。我们提议估算模型组成部分之间的共享随机效应,以揭示和解释野火活动不同方面共同的驱动因素。这将导致增加偏差和减少估计不确定性,同时进行更好的预测。具体地标分度的次模型和从零点算的预测活动为我们进行大规模的预测活动,通过零度的预测活动提供了大规模观测和预测活动,并解释。