All-hazards dataset mined from the US National Incident Management System 1999-2020.

This paper describes a dataset mined from the public archive (1999-2020) of the US National Incident Management System Incident Status Summary (ICS-209) forms (a total of 187,160 reports for 35,170 incidents, including 34,478 wildland fires). This system captures detailed daily/regular information on incident development and response, including social and economic impacts. Most (98.4%) reports are wildland fire-related, with other incident types including hurricane, hazardous materials, flood, tornado, search and rescue, civil unrest, and winter storms. The archive, although publicly available, has been difficult to use for research due to multiple record formats, inconsistent data entry, and no clean pathway from individual reports to high-level incident analysis. Here, we describe the open-source, reproducible methods used to produce a science-grade version of the data, including formal connections made to other published wildland fire data products. Among other applications, this integrated and spatially augmented dataset enables exploration of the daily progression of the most costly, damaging, and deadly environmental-hazard events in recent US history.

All-hazards dataset mined from the US National Incident Management System 1999-2014.

This paper describes a new dataset mined from the public archive (1999-2014) of the U.S. National Incident Management System/Incident Command System Incident Status Summary Form (a total of 124,411 reports for 25,083 incidents, including 24,608 wildfires). This system captures detailed information on incident management costs, personnel, hazard characteristics, values at risk, fatalities, and structural damage. Most (98.5%) of the reports are fire-related, followed in decreasing order by other, hurricane, hazardous materials, flood, tornado, search and rescue, civil unrest, and winter storms. The archive, although publicly available, has been difficult to use due to multiple record formats, inconsistent free-form fields, and no bridge between individual reports and high-level incident analysis. Here, we describe this improved dataset and the open, reproducible methods used, including merging records across three versions of the system, cleaning and aligning with the current system, smoothing values across reports, and supporting incident-level analysis. This integrated record offers the opportunity to explore the daily progression of the most costly, damaging, and deadly events in the U.S., particularly for wildfires.

Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima.

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.