Wildfires can be detrimental to urban and rural communities, causing impacts in the form of psychological stress, direct physical injury, and smoke-related morbidity and mortality. This study examined the area burned by wildfires over the entire state of California from the years 2000 to 2020 in order to quantify and identify whether burned area and fire frequency differed across Census tracts according to socioeconomic indicators over time. Wildfire data were obtained from the California Fire and Resource Assessment Program (FRAP) and National Interagency Fire Center (NIFC), while demographic data were obtained from the American Community Survey. Results showed a doubling in the number of Census tracts that experienced major wildfires and a near doubling in the number of people residing in wildfire-impacted Census tracts, mostly due to an over 23,000 acre/year increase in the area burned by wildfires over the last two decades. Census tracts with a higher fire frequency and burned area had lower proportions of minority groups on average. However, when considering Native American populations, a greater proportion resided in highly impacted Census tracts. Such Census tracts also had higher proportions of older residents. In general, high-impact Census tracts tended to have higher proportions of low-income residents and lower proportions of high-income residents, as well as lower median household incomes and home values. These findings are important to policymakers and state agencies as it relates to environmental justice and the allocation of resources before, during, and after wildfires in the state of California.

Identification of two distinct fire regimes in Southern California: implications for economic impact and future change View the table of contents for this issue, or go to the journal homepage for more 2015 Environ. Res. Lett. 10 094005

A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs) and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer’s accuracy of 96.3% for the classification Random Forest (CRF) and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF). The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought.

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.

California’s Sierra Nevada has experienced a large increase in wildfire activities over recent decades. This intensifying fire regime has coincided with a warming climate and increasing human activity, but the relative importance of the biophysical and anthropogenic drivers of wildfire remains unclear across this diverse landscape, especially at a finer spatial scale. We used multisource geospatial data sets of fire occurrence, and human, climatic, and biophysical variables to examine the spatial pattern and controls on Sierra Nevada wildfires averaged from 1984 to 2017. The maximum entropy model driven by both biophysical and anthropogenic variables predicted the spatial distribution of fire probability well, with an area under the curve (AUC) score of 0.81. Model diagnostics revealed that aspects of the climate, including vapor pressure deficit (VPD), temperature, and burning index (difficulty of control), dominated the spatial patterns of fire probability across the whole Sierra Nevada region. The VPD was the leading control, with a relative contribution of 32.1%. Population density and fuel amount were also significant drivers, each accounting for 15.8%–12.4% of relative contribution. VPD and burning index were the most important factors for fire probability in higher elevation forest, while population density was comparatively more important in the lower elevation forest regions of the Sierra Nevada. Our findings improved our understanding of the relative importance of various factors in shaping the spatial patterns of historical fire probability in the Sierra Nevada and across various subecoregions, providing insights for targeting spatially varying forest management strategies to limit potential future increases in wildfires.