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.

中文翻译：

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在 Firelihood 的概率贝叶斯框架中预测区域野火活动

对野火活动进行建模对于为基于科学的风险管理提供信息和了解世界范围内易发生火灾的生态系统的时空动态至关重要。模型有助于理清不同因素的相对影响，了解野火的可预测性，并提供对特定事件的洞察。在这里，我们开发了 Firelihood，这是一个双分量、贝叶斯、分层结构的日常火灾活动概率模型，它被建模为标记点过程的结果：单个火灾是点（发生分量），火灾大小是标记（尺寸分量）。使用集成嵌套拉普拉斯近似 (INLA) 结合随机偏微分方程 (SPDE) 方法，将发生的时空泊松模型调整为网格火灾计数。大小模型基于分段估计的帕累托分布和广义帕累托分布，并使用 INLA 进行调整。火灾天气指数 (FWI) 和森林面积是主要的解释变量。包括时空残差，以提高天气与火灾发生关系的一致性。贝叶斯模型的后验分布提供了 1,000 次火灾活动的复制，并将其与法国地中海不同时间和空间尺度的观察结果进行了比较。该地区超过 1 公顷的火灾数量以每日为粗略重现，每周或更长时间进行更准确的预测。还预测了区域每周较大火灾总数（10-100 公顷），但精度随着规模的扩大而降低，因为模型的不确定性随着事件罕见性的增加而增加。火灾数量或燃烧区域的局部预测也需要更长的聚合期来保持模型的准确性。对大于 1 公顷的火灾的估计也与 2003 年史无前例的热浪极端火灾季节期间的观察结果一致，但该模型系统性地低估了大火和燃烧区域，这表明 FWI 并未始终如一地对大火灾的实际危险进行评估。热浪期间发生火灾。Firelihood 实现了对火灾危险潜在随机性的新颖分析，并提供了多种应用，包括在气候变化背景下进行管理和预测的火灾危险预测。对大于 1 公顷的火灾的估计也与 2003 年史无前例的热浪极端火灾季节期间的观察结果一致，但该模型系统性地低估了大火和燃烧区域，这表明 FWI 并未始终如一地对大火灾的实际危险进行评估。热浪期间发生火灾。Firelihood 实现了对火灾危险潜在随机性的新颖分析，并提供了多种应用，包括在气候变化背景下进行管理和预测的火灾危险预测。对大于 1 公顷的火灾的估计也与 2003 年史无前例的热浪极端火灾季节期间的观察结果一致，但该模型系统性地低估了大火和燃烧区域，这表明 FWI 并未始终如一地对大火灾的实际危险进行评估。热浪期间发生火灾。Firelihood 实现了对火灾危险潜在随机性的新颖分析，并提供了多种应用，包括在气候变化背景下进行管理和预测的火灾危险预测。