Title:

Spatial datasets of probabilistic wildfire risk components for the conterminous United States (270m) for circa 2011 climate and projected future climate circa 2047

Author(s):

Riley, Karin L.; Zimmer, Scott N.; Kodra, Evan; Grenfell, Isaac C.; Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Jolly, W. Matthew; Brittain, Stuart E.; Callahan, Michael N.

Publication Year:

2025

How to Cite:

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Riley, Karin L.; Zimmer, Scott N.; Kodra, Evan; Grenfell, Isaac C.; Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Jolly, W. Matthew; Brittain, Stuart E.; Callahan, Michael N. 2025. Spatial datasets of probabilistic wildfire risk components for the conterminous United States (270m) for circa 2011 climate and projected future climate circa 2047. Updated 01 July 2025. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2025-0006

Users are strongly encouraged to read and fully comprehend the metadata prior to data use. Users should acknowledge the Originator when using this dataset as a source. Users should share data products developed using the source dataset with the Originator. No warranty is made by the Originator as to the accuracy, reliability, or completeness of these data for individual use or aggregate use with other data, or for purposes not intended by the Originator. This dataset is intended to estimate probabilistic wildfire risk components that can support national strategic planning. The applicability of the data to support fire and land management planning on smaller areas will vary by location and specific intended use. Further investigation by local and regional experts should be conducted to inform decisions regarding local applicability. It is the sole responsibility of the local user, using this metadata document and local knowledge, to determine if and/or how these data can be used for particular areas of interest. National FSim products are not intended to replace local products where they exist, but rather serve as a back-up by providing wall-to-wall cross-boundary data coverage. It is the responsibility of the user to be familiar with the value, assumptions, and limitations of these national data publications. Managers and planners must evaluate these data according to the scale and requirements specific to their needs. Spatial information may not meet National Map Accuracy Standards. This information may be updated without notification.

Abstract:

The Large Fire Simulation System (FSim) simulates the growth and behavior of hundreds of thousands of fire events for risk analysis using geospatial data on historical fire occurrence, weather, terrain, and fuel conditions. It can be used to model the frequency and intensity of fires across large spatial and temporal scales. We simulated fire activity in FSim across the conterminous United States with a 2020 landscape (LANDFIRE) and under two sets of climate conditions: 1) using recent climate patterns from 2004-2018 and 2) with modeled future climate conditions for 2040-2054 to address how fire activity may change under future climate. The purpose of this research is to address how climate itself is expected to impact fire activity. Changes in climate will impact the number of days with conditions that promote burning and affect the intensity of burning conditions, which will impact ultimate fire activity and behavior.

The data presented here represent modeled burn probability (BP) and conditional flame length probabilities (FLPs) for the conterminous United States (CONUS) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels (FIL). The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 - < 4 ft; FLP3 = 4 - < 6 ft; FLP4 = 6 - < 8 ft; FLP5 = 8 - < 12 ft; FLP6 = 12+ ft. Since they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment. All calibration settings and input data used in this analysis, such as vegetation and fuels, were the same as those used in the prior 2020-landscape 2011-climate vintage national FSim run, Dillon et al. (2023) (referred to in the remainder of this document as either the "2020 national run" or the "2011 climate run").

The 2020 national run published here is distinct from the preceding one in two ways: 1) in the previously published version, burnable pixels that did not burn during any simulations were backfilled with the low burn probability of 0.00008 and were assigned flame length probabilities; in this publication we do not alter outputs in this way; and 2) a complete set of flame length and arrival time data are available for all simulated fires in this version. Because of stochasticity in FSim, the two 2020 national runs have minor differences in some areas.

The weather streams used in the circa 2047 climate simulation were updated to a 15-year climate period centered on 2047 using projected shifts in monthly temperature, precipitation, and relative humidity from an ensemble of six General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Burn probabilities and flame length probabilities from this circa 2047 run were compared to those from the 2020 national FSim run to assess expected changes at the scale of counties and pyromes, or areas of homogeneous fire regime.

Keywords:

biota; climatologyMeteorologyAtmosphere; environment; Climate change; Climate change effects; Carbon; Ecology, Ecosystems, & Environment; Fire; Natural Resource Management & Use; fire; climate change; burn probability; wildfire risk; United States; CONUS

Related publications:

• Short, Karen C.; Finney, Mark A.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2016. Spatial dataset of probabilistic wildfire risk components for the conterminous United States. 1st Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034
• Short, Karen C.; Finney, Mark A.; Vogler, Kevin C.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2020. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-2
• Dillon, Gregory K.; Scott, Joe H.; Jaffe, Melissa R.; Olszewski, Julia H.; Vogler, Kevin C.; Finney, Mark A.; Short, Karen C.; Riley, Karin L.; Grenfell, Isaac C.; Jolly, W. Matthew; Brittain, Stuart E. 2023. Spatial datasets of probabilistic wildfire risk components for the United States (270m). 3rd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034-3
• Columbia Climate School, National Center for Disaster Preparedness. 2025. U.S. natural hazards climate change projections. Columbia Climate School, National Center for Disaster Preparedness. https://ncdp.columbia.edu/us-natural-hazards-and-climate-change/

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