A Protocol for Collecting Burned Area Time Series Cross-Check Data

Data on wildfire growth are useful for multiple research purposes but are frequently unavailable and often have data quality problems. For these reasons, we developed a protocol for collecting daily burned area time series from the InciWeb website, Incident Management Situation Reports (IMSRs), and other sources. We apply this protocol to create the Warehouse of Multiple Burned Area Time Series (WoMBATS) data, which are a collection of burned area time series with cross-check data for 514 wildfires in the United States for the years 2018–2020. We compare WoMBATS-derived distributions of wildfire occurrence and size to those derived from MTBS data to identify potential biases. We also use WoMBATS data to cross tabulate the frequency of missing data in InciWeb and IMSRs and calculate differences in size estimates. We identify multiple instances where WoMBATS data fails to reproduce wildfire occurrence and size statistics derived from MTBS data. We show that WoMBATS data are typically much more complete than either of the two constituent data sources, and that the data collection protocol allows for the identification of otherwise undetectable errors. We find that although disagreements between InciWeb and IMSRs are common, the magnitude of these differences are usually small. We illustrate how WoMBATS data can be used in practice by validating two simple wildfire growth forecasting models

Multi-Model Forecasts of Very-Large Fire Occurences during the End of the 21st Century

Climate change is anticipated to influence future wildfire activity in complicated, and potentially unexpected ways. Specifically, the probability distribution of wildfire size may change so that incidents that were historically rare become more frequent. Given that fires in the upper tails of the size distribution are associated with serious economic, public health, and environmental impacts, it is important for decision-makers to plan for these anticipated changes. However, at least two kinds of structural uncertainties hinder reliable estimation of these quantities—those associated with the future climate and those associated with the impacts. In this paper, we incorporate these structural uncertainties into projections of very-large fire (VLF)—those in the upper 95th percentile of the regional size distribution—frequencies in the Continental United States during the last half of the 21st century by using Bayesian model averaging. Under both moderate and high carbon emission scenarios, large increases in VLF frequency are predicted, with larger increases typically observed under the highest carbon emission scenarios. We also report other changes to future wildfire characteristics such as large fire frequency, seasonality, and the conditional likelihood of very-large fire events