Wildfire Danger Potential in California

Abstract:

Wildfires are an important concern in California (CA) which is characterized by the semi-arid to arid climate and vegetation types. Highly variable winter precipitation and extended hot and dry warm season in the region challenge an effective strategic fire management. Climatologically, the fire season which is based on live fuel moisture (LFM) of generally below 80% in Los Angeles County spans 4 months from mid-July to mid-November, but it has lasted over 7 months in the past several years. This behavior is primarily due to the ongoing drought in CA during the last decade, which is responsible for frequent outbreaks of severe wildfires in the region. Despite their importance, scientific advances for the recent changes in wildfire risk and effective assessments of wildfire risk are lacking. In the present study, we show impacts of large-scale atmospheric circulations on an early start and then extended length of fire seasons. For example, the strong relationships of North Atlantic Oscillation (NAO) with springtime temperature and precipitation in the SWUS that was recently revealed by our team members have led to an examination of the possible impact of NAO on wildfire danger in the spring. Our results show that the abnormally warm and dry spring conditions associated with positive NAO phases can cause an early start of a fire season and high fire risks throughout the summer and fall. For an effective fire danger assessment, we have tested the capability of satellite vegetation indices (VIs) in replicating in situ LFM of Southern CA chaparral ecosystems by 1) comparing seasonal/interannual characteristics of in-situ LFM with VIs and 2) developing an empirical model function of LFM. Unlike previous studies attempting a point-to-point comparison, we attempt to examine the LFM relationship with VIs averaged over different areal coverage with chamise-dominant grids (i.e., 0.5 km to 25 km radius circles). Lastly, we discuss implications of the results for fire danger assessment and prediction.