发表机构
University of Colorado Boulder; Jupiter Intelligence（科罗拉多大学博尔德分校; 木星智能）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出分层统计框架FireGen，模拟1984-2023年北加州野火的发生、几何与过火面积，支持蒙特卡洛模拟，用于量化气候变异下的野火风险。
AI 中文摘要
北加州的野火制度表现出强烈的时空变异性、重尾的火灾规模分布以及对气候条件的敏感性。我们开发了一个分层统计框架，用于模拟1984年至2023年北加州的野火发生、几何形状和过火面积。火灾质心被建模为一个具有协变量驱动强度的时空点过程，并通过基于乘性伽马分布的随机冲击进行扩展，以更好地捕捉月度火灾次数的分布。在给定火灾位置的情况下，过火面积多边形使用参数化椭圆模型表示，该模型分离了尺度、形状和方向，椭圆参数从标准化火灾周界估计。总过火面积使用异方差对数正态回归建模，该回归包含气候和空间协变量，包括蒸汽压亏缺异常。该框架支持野火过程的蒙特卡洛模拟，并为在观测到的气候变异性下评估野火风险和累积过火面积提供了概率工具。
英文摘要
Wildfire regimes in Northern California exhibit strong spatio-temporal variability, heavy-tailed fire size distributions, and sensitivity to climatic conditions. We develop a hierarchical statistical framework to model wildfire occurrence, geometry, and burned area in Northern California from 1984-2023. Fire centroids are modeled as a spatio-temporal point process with covariate-driven intensity, extended by a multiplicative gamma-based stochastic shock to better capture the distribution of monthly fire counts. Conditional on fire location, burned area polygons are represented using a parametric ellipse model that separates scale, shape, and orientation, with ellipse parameters estimated from standardized fire perimeters. Total burned area is modeled using a heteroskedastic lognormal regression incorporating climatic and spatial covariates, including vapor pressure deficit anomalies. The framework enables Monte Carlo simulation of wildfire processes and provides a probabilistic tool for assessing wildfire risk and cumulative burned area under observed climate variability.
Comments40 pages, plus 38 pages of supplementary material
