Quantifying the Influence of Meteorological Factors on Wildfire Occurrences and Impacts in Diverse Californian Climates

In recent years, the escalation of extreme weather events has notably amplified the frequency and intensity of wildfires, rendering them a critical concern. Within the context of California, a region that experiences a significant number of wildfires annually, the impact on both ecosystems is substantial. This is particularly pronounced given the diverse climatic zones spanning the state, encompassing a broad spectrum of vegetation patterns and hydrometeorological conditions, thereby leading to discernible variations in wildfire occurrences across distinct geographic areas.

However, a prominent challenge faced by wildfire researchers lies in the dearth of comprehensive forest area data. In response, this study adopts satellite data as a supplement to enhance the precision of time series analyses and the comprehensive evaluation of pertinent variables. This augmented dataset facilitates a more holistic comprehension of forest dynamics. By employing the pseudo-Tri-Dimensional complementary ensemble empirical mode decomposition (Pseudo-TCEEMD) methodology, this investigation delves into the intricate spatiotemporal distribution of variables, the meteorological determinants intertwined with wildfire events, and the subsequent ramifications of these fires on forest vegetation. The insights gleaned from this analysis stem from an exhaustive study of satellite image time-series data. Furthermore, to dissect the temporal evolution of wildfire incidents, a time–frequency analytical tool, the complementary ensemble empirical mode decomposition, is deftly applied.

It is noteworthy that wildfires can be triggered by a range of factors, including natural occurrences such as lightning strikes and human-induced activities. To elucidate the intrinsic correlations between meteorological variables and the ignition of wildfire events, a meticulous scale- and time-dependent correlation methodology, termed time-dependent intrinsic correlation (TDIC), is effectively implemented. Additionally, the study accounts for temporal lag effects through the utilization of time-dependent intrinsic cross-correlation (TDICC), thereby comprehensively assessing the far-reaching consequences of wildfires, spanning impacts on air and water quality, as well as implications for human health.

At its core, this study is dedicated to quantitatively unraveling the intricate interplay between wildfires and the multifaceted aftermath they precipitate. Beyond this, a pivotal facet of this research is the discernment of regional determinants that intricately influence the spatial and temporal distribution of wildfire occurrences within the diverse landscape of California.