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Relationships of climate, human activity, and fire history to spatiotemporal variation in annual fire probability across California: Source Code and Core Data

Cite this dataset

Park, Isaac et al. (2021). Relationships of climate, human activity, and fire history to spatiotemporal variation in annual fire probability across California: Source Code and Core Data [Dataset]. Dryad.


In the face of recent wildfires across the Western United States, it is essential that we understand both the dynamics that drive the spatial distribution of wildfire, and the major obstacles to modeling the probability of wildfire over space and time.  However, it is well documented that the precise relationships of local vegetation, climate, and ignitions, and how they influence fire dynamics, may vary over space and among local climate, vegetation, and land use regimes.  This raises questions not only as to the nature of the potentially nonlinear relationships between local conditions and the fire, but also the possibility that the scale at which such models are developed may be critical to their predictive power and to the apparent relationship of local conditions to wildfire.  In this study we demonstrate that both local climate – through limitations posed by fuel dryness (CWD) and availability (AET) – and human activity – through housing density, roads, electrical infrastructure, and agriculture, play important roles in determining the annual probabilities of fire throughout California.  We also document the importance of previous burn events as potential barriers to fire in some environments, until enough time has passed for vegetation to regenerate sufficiently to sustain subsequent wildfires. We also demonstrate that long-term and short-term climate variations exhibit different effects on annual fire probability, with short-term climate variations primarily impacting fire probability during periods of extreme climate anomaly.  Further, we show that, when using nonlinear modeling techniques, broad-scale fire probability models can outperform localized models at predicting annual fire probability.  Finally, this study represents a powerful tool for mapping local fire probability across the state of California under a variety of historical climate regimes, which is essential to avoided emissions modelling, carbon accounting, and hazard severity mapping for the application of fire-resistant building codes across the state of California.


Climate data used in this study was drawn from the California Basin Characterization Model v8, and consists of monthly estimates of cumulative water deficit (CWD) and actual evapotranspiration (AET) from 1951 – 2016.  This dataset represents a 270-m grid-based model of water balance calculations that incorporates climate inputs through PRISM data in addition to solar radiation, topographic shading, cloudiness, and soil properties to estimate evapotranspiration.  Using these monthly values, we calculated the 1980 – 2009 mean CWD and AET normals, as well as mean deviations from those normals over a three-year period preceding each year of interest.

Cultivated and agricultural areas were identified using the 2016 National Land Cover Database data, which estimated dominant land cover throughout North America at 30-m resolution.  The proportion of cultivated area and of water features that covered each 1-km pixel were then calculated by resampling to 1-km scale. Mean housing density data was drawn from the Integrated Climate and Land-Use Scenarios (ICLUS) dataset, which provides decadal estimates of housing density throughout the United states from 1970 - 2020.  As precise continuous estimates of housing density were not available, housing density within each pixel was set to the mean of its class.  Annual values were estimated from decadal data using linear interpolation.  Ecoregions within California (hereafter referred to as “regions”) were delineated using CalVeg ecosystem provinces data.

Road data were drawn from 2018 TIGER layer data, and consisted of all primary and secondary roads across California. Electrical infrastructure data was drawn from 2020 transmission lines data.  In both cases, the distance of nearest roads or transmission lines to each pixel were then calculated.  Pixels which contained roads or electrical infrastructure were assigned distances of 0 km.

Fire history data was drawn from FRAP fire perimeter data, which incorporates perimeters of all known timber fires >10 acres (>0.04 km2), brush fires >30 acres (>0.12 km2), and grass fires >300 acres (>1.21 km2) from 1878 – 2017. Using this data, the presence of fire in each 1-km pixel was classified in a binary fashion (e.g. 1 for burned, 0 for unburned) for each year of interest.  Due to computational limits and the quantity of data involved in this study, we did not calculate the burned area within each pixel, or distinguish pixels in which a single fire occurred in a given year from those in which multiple fires occurred.   This data was also used to calculate the number of years since the most recent fire within any pixel, prior to each year in which fire probability was projected.  Thus, locations in which no fire was observed throughout the fire record were treated as having gone a maximum of 100 years without a fire event for the purposes of model construction. These pixels comprised 29% - 33% of data annually (depending on year), and included both locations in which fire would not be expected (such as highly xeric regions) as well as locations in fire-prone areas in which no fire had been documented within the FRAP fire perimeter data used in this study. 

Usage notes

Please refer to Readme.txt file.


California Department of Forestry and Fire Protection, Fire and Resource Assessment Program, Award: 8CA03698