Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Fire Ignition Probability * Tier: 3 * Data Vintage: 1992 to 2015 * Unit Of Measure: Probability, 0-1 * Metric Definition and Relevance: These rasters depict the predicted human- and lightning-caused ignition probability for the state of California. Ignition is regulated by complex interactions among climate, fuel, topography, and humans. Considerable studies have advanced our knowledge on patterns and drivers of total areas burned and fire frequency, but much is less known about wildfire ignition. To better design effective fire prevention and management strategies, it is critical to understand contemporary ignition patterns and predict the probability of wildfire ignitions from different sources. UC Davis researchers modeled and analyzed human- and lightning-caused ignition probability across the whole state and sub-ecoregions of California, USA. Findings reinforce the importance of varying humans vs biophysical controls in different fire regimes, highlighting the need for locally optimized land management to reduce ignition probability. Based on the most complete ignition database available, researchers developed maximum entropy models to predict the spatial distribution of long-term human- and lightning-caused ignition probability at 1 km and investigated how a set of biophysical and anthropogenic variables controlled their spatial variation in California and across its sub-ecoregions. Results showed that the integrated models with both biophysical and anthropogenic drivers predicted well the spatial patterns of both human- and lightning-caused ignitions in statewide and sub-ecoregions of California. Model diagnostics of the relative contribution and marginalized response curves showed that precipitation, slope, human settlement, and road network were the most important variables for shaping human-caused ignition probability, while snow water equivalent, lightning density, and fuel amount were the most important variables controlling the spatial patterns of lightning-caused ignition probability. The relative importance of biophysical and anthropogenic predictors differed across various sub-ecoregions of California. * Creation Method: Maximum entropy models were developed to estimate wildfire ignition probability and understand the complex impacts of anthropogenic and biophysical drivers, based on a historical ignition database. UC Davis researchers developed maximum entropy models to estimate wildfire ignition probability and understand the complex impacts of anthropogenic and biophysical drivers, based on a historical ignition database. Researchers used the US Forest Service Fire Program Analysis-Fire Occurrence Database (FPA- FOD), compiled from reporting systems of US federal, state, and local fire agencies (Short [2017](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib44)). This homogenized and comprehensive dataset includes wildfire ignition records on both public and private lands from 1992 to 2015, and accounted for many small fires that are not included in many other fire datasets. Researchers used spatial layers of population density, transportation road network, and nighttime lights, to quantify human settlement and accessibility. Researchers assembled statewide geospatial layers to evaluate the biophysical controls from topography, climate, and fuels on spatial variation of wildland ignitions (table [1](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dat1)). The 2010 global 250 m terrain elevation data (GMTED2010) was used to characterize slope and aspect at 1 km spatial resolution. Weather information came from the gridded Daily Surface Weather and Climatological Summaries meteorological data at 1 km (Daymet) (Thornton _et al_ [2020](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib50)), including precipitation (Prcp), minimum and maximum temperature (Tmin and Tmax), incident shortwave radiation (Srad), water vapor pressure (VP), and snow water equivalent (SWE), or the amount of water that would be released from melting snowpack. We derived long-term annual means during 1992–2015 for these meteorological variables at 1 km. Researchers modeled the spatial pattern of ignition probability using the maximum entropy statistical method (MaxEnt v3.3.3k) (Phillips _et al_ [2004](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib39), [2006](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib38), [2021](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib40)). MaxEnt is a machine-learning technique originally designed to model species distribution from presence-only data using multidimensional environmental inputs (Phillips _et al_ [2004](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib39), [2006](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60dabib38)). It estimates a target probability distribution by iteratively searching for the probability distribution with maximum entropy (i.e. the one that is most uniform), subject to the environmental variables at each observation (i.e. presence-only point). The models captured well the spatial patterns of human and lightning started wildfire ignitions in California. The human-caused ignitions dominated the areas closer to populated regions and along the traffic corridors. Model diagnosis showed that precipitation, slope, human settlement, and road network shaped the statewide spatial distribution of human-started ignitions. In contrast, the lightning-caused ignitions were distributed more remotely in Sierra Nevada and North Interior, with snow water equivalent, lightning strike density, and fuel amount as primary drivers. Separate region-specific model results further revealed the difference in the relative importance of the key drivers among different sub-ecoregions. * Credits: Bin Chen and Yufang Jin, University of California Davis, [bch@ucdavis.edu](mailto:bch@ucdavis.edu) [Spatial patterns and drivers for wildfire ignitions in California - IOPscience](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60das5) [Short K C 2017 Spatial wildfire occurrence data for the United States, 1992-2015 [FPA_FOD_20170508]](https://iopscience.iop.org/article/10.1088/1748-9326/ac60da/meta#erlac60das5)