DOI: 10.25349/d96w4w
title: Relationships of climate, human activity, and fire history to spatiotemporal variation in annual fire probability across California: Source Code and Core Data

description: 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.
description: 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 &gt;10 acres (&gt;0.04 km<sup>2</sup>),
 brush fires &gt;30 acres (&gt;0.12 km<sup>2</sup>),
 and grass fires &gt;300 acres (&gt;1.21
 km<sup>2</sup>) 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. 
description: Please refer to Readme.txt file.