Wildfire predictions for near-term operations versus long-term planning and research operate at different spatiotemporal scales, aiming either to understand the risk posed over the course of an individual fire or fire season or to understand the broadscale characteristics of fire regimes. For example, operational needs emphasize contemporary, on-the-ground conditions (Brillinger et al., 2003; Martell et al., 1989; Sullivan, 2009a, b) and largely ignore the longer-term controls on fire (e.g., occurring years to decades prior to a fire). By contrast, predictions across longer time frames and often larger spatial scales will omit the contemporary weather patterns that drive fire occurrence (Krawchuk and Moritz, 2014; Littell et al., 2009; Urbieta et al., 2015). While many models and datasets exist to support these needs, they also reflect different and non-overlapping scales. We sought to fill this gap by developing a dataset of the predicted conditional probability that an area on the landscape will burn in a large wildfire (i.e., >405 ha) given an ignition or fire spread to that area, which integrates across spatiotemporal scales in an empirical framework. We developed the dataset at a high spatial resolution (250 m pixel) and moderate temporal resolution (updated weekly) across forests and woodlands in the contiguous western US. The resulting dataset is intended to meet multiple objectives of local to national research, management, and planning efforts.

The dataset that we describe in this paper is continually updated with near-term information, which we define as occurring over a period of days to months prior to and during a fire. A well-developed approach to similarly incorporate the dynamic near-term drivers of wildfires is to simulate the spread of individual fires over a landscape (Finney, 2004; Sullivan, 2009c; Tymstra et al., 2010). Modeling systems that perform these simulations, such as Farsite (Finney, 2004) and FSPro (Finney et al., 2011b), are used widely during wildfire incidents and in real time to understand the potential spread and behavior of burning fires. These tools can provide critical information for individual or localized fire probability in real time but are limited in their ability to elucidate regional and cross-regional fire risk at similar time frames and are dependent on fuels data, e.g., from the LANDFIRE project (Rollins, 2009), which are often not updated for years at a time. Although the work described herein does not attempt to model the risk posed by individual fires, it is meant to provide contemporary fire information across regional extents, drawing on continually updated fuel and weather data to predict conditional large fire probability at a high resolution. Therefore, it provides a needed, complementary dataset to existing models that operate on near-term timescales.

By simulating individual fires across time and space, the fire modeling systems described above can also scale up to predict the long-term, multiyear potential of fires at every point on a landscape (Finney et al., 2011a; Parisien et al., 2005). This approach is commonly used for the longer-term planning of fuel treatments and other fire risk planning and assessments (Haas et al., 2013; Thompson et al., 2017). However, these landscape-scale simulations can be user and computationally intensive (Parisien et al., 2012a; Varner et al., 2009), constraining the ability of analysts and planners to update datasets at both broad spatial scales and decision-relevant timescales. For example, regional or national predictive datasets may need to be updated according to changes in fuel that occur within a fire season and on an interannual basis.

Alternative methods to predict fire occurrence relate empirical fire data to environmental predictors in statistical models (Gray et al., 2014; Preisler et al., 2016; Stavros et al., 2014). Data availability in this case, namely the spatiotemporal alignment of accurate and high-resolution fire, weather, and fuels data, also acts as a constraint on either the spatial or temporal scale of analysis (Taylor et al., 2013). However, such statistical methods are common in predicting fire occurrence on a macroscale because they can draw on coarse-scale data to overcome this constraint (Krawchuk et al., 2009; Moritz et al., 2012; Parisien et al., 2012b). Owing to the flexibility of model specification and data inputs as well as increasingly accurate and high-resolution observational data, statistically based empirical models can integrate both the contemporary, near-term drivers as well as the long-term controls on fire potential.

Indeed, recent studies have explicitly compared the role of the temporal scale in predicting fire occurrence and have shown that long-term normals and variability in climate and vegetation provide complementary predictive power (Abatzoglou and Kolden, 2011, 2013; Parisien et al., 2014; Riley et al., 2013). For example, the long-term climate exerts an influence on the flammability (e.g., due to biomass production, vegetation composition, and average fuel moisture) of a fuel bed, but weekly and sub-weekly weather will moderate fuel moisture in a site-specific way. Similarly, relatively recent disturbance events such as previous burns can regulate biomass production and the subsequent fire risk on interannual timescales (Parisien et al., 2014; Parks et al., 2015). It follows that predictive datasets of wildfire potential should strive to integrate across complex, dynamic interactions at near- and long-term timescales. Here, we describe a time series of the conditional probability of a large fire, continually updated on a weekly basis (with a 1 week lag) to integrate the near-term controls on fire occurrence, which also considers the longer-term influences of land use, disturbance, the climate, and topography. The complete dataset (2005–present) can also be considered a foundational dataset for understanding the long-term, probabilistic exposure of forests and woodlands to large fires.

Using all training data from 2005 to 2014 (i.e., no independent testing data), we compared models in R using the “caret” package (Kuhn, 2008), and extracted variable importance using the “rfpimp” package in Python (available at https://github.com/parrt/random-forest-importances, last access: 14 September 2018). We ranked predictor variable importance based on the permutation importance, which directly measures importance by observing the effect on model accuracy by randomly permuting the values of each predictor variable (Cutler et al., 2007). Since RF “spreads” variable importance across collinear variables (Cutler et al., 2007), we used a built-in function in the “rfpimp” package to permute collinear variables together and determine their relative and collective importance (Fig. 3). Across the 10 models, overall accuracy was consistently between 0.77 and 0.79 and area under the receiver operating curve (AUC) was consistently 0.83–0.86. Out of 46 total predictor variables, the most important variables were near-term weather variables that included the ERC, BI, FM100, FM1000, relative humidity, and precipitation, as well as the collective near- and long-term EVI, NDWI, and LST variables (Fig. 3).

To independently evaluate the model on data from 2015 to 2016, we used the MODIS BA and FOD datasets to draw a testing sample from within all large fires and an equally sized random sample of small fires (response value of “0” and “1”, respectively; n≅400 large fires). Again, large samples were taken as the centroid of 500 m pixels. Using weekly predictions (i.e., raster maps; Fig. 5) of large fire probability in 2015 and 2016, we extracted the predicted values at the time (i.e., the closest prediction in time prior to fire occurrence) and location of individual testing points. We used the R package “OptimalCutpoints” (López-Ratón et al., 2014) to determine an optimal cutoff between 0 and 1 that simultaneously maximized the sensitivity (true positive rate) and specificity (true negative rate) of predictions. In this case, using a probability cutoff of 0.47 to predict binary large (>0.47) versus small (<0.47) fires resulted in the greatest rate of true positives and negatives in our testing datasets. Based on an optimal cutoff of 0.47 and 2 years of independent data, the sensitivity of the dataset was 0.76, the specificity was 0.75, and the area under the receiver operating curve (ROC) curve was 0.82 (Fig. 4). We took another step to visualize model performance by mapping the rate of false positives and false negatives (i.e., the number of false positives or false negatives normalized by the number of testing samples) within each EPA level III ecoregion to examine any obvious biases in under or overprediction across ecoregions (Fig. 6). There was more of a tendency for the model to overpredict large fires in some of the drier ecoregions, such as the Colorado Plateau and the Central Basin and Range, and the inverse was true in some of the wetter ecoregions. In particular, the Cascades and Southern Rockies tended to underpredict large fires rather than overpredict (Fig. 6).

We developed a continuous integration (CI) “pipeline” to generate new predictions as soon as the dynamic predictors upon which the model is conditioned become available in GEE. The refresh rate of each predictor varies based on the data sources. For example, GRIDMET assets are updated approximately every 2 days, whereas the MODIS products are updated approximately every 8 days. The pipeline, which tests for the availability of predictors against the requirements of the model, runs on a schedule, compiling each morning at 04:00 Pacific standard time. If all of the criteria are met, a new prediction is generated and appended to the existing collection. We used GitLab.com because GitLab offers continuous integration (CI) services at no cost. The builds are executed using a custom Docker image, which is a bare-bones Ubuntu image configured with the Google Earth Engine Python application program interface (API) client library and its dependencies.

Each image in the dataset contains the following bands:

Band 1 (“mean”) represents the mean probability of large fires across 10 trained models. Values range from 0 to 1.