Spatiotemporal Analysis of California Wildfires: Trends, Variability, and Meteorological Correlations

This research examines the spatiotemporal trends and variability of wildfires in California based on wildfire data from 2000 to 2022. California, a region that experiences hundreds of wildfires each year, faces increasing risks due to the rising intensity and frequency of these events, driven by varied climates, vegetation patterns, and hydro-meteorological conditions. To address the challenge of limited forest area data in wildfire studies, this research employs satellite data to better understand forest conditions. The study applies Pseudo-Tri-Dimensional Complementary Ensemble Empirical Mode Decomposition (Pseudo-TCEEMD) to analyze the distribution of wildfires across time and space. Additionally, Complementary Ensemble Empirical Mode Decomposition is used to investigate the evolving patterns and trends of wildfire occurrences. Recognizing that California's wildfires are triggered by both natural causes, such as lightning, and human activities, the study utilizes Time-Dependent Intrinsic Correlation (TDIC) to explore the relationships between meteorological variables and wildfire events, with a focus on their spatiotemporal dynamics.