The Construction of Probabilistic Wildfire Risk Estimates for Individual Real Estate Parcels for the Contiguous United States

Abstract

The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented. The FSF-WFM integrates several existing methods from the wildfire science community and implements computationally efficient and scalable modeling techniques to allow for new high-resolution, CONUS-wide hazard generation. Burn probability, flame length, and ember spread for the years 2022 and 2052 are computed from two ten-year representative Monte Carlo simulations of wildfire behavior, utilizing augmented LANDFIRE fuel estimates updated with all the available disturbance information. FSF-WFM utilizes ELMFIRE, an open-source, Rothermel-based wildfire behavior model, and multiple US Federal Government open data sources to drive the simulations. LANDFIRE non-burnable fuel classes within the wildland–urban interface (WUI) are replaced with fuel estimates from machine-learning models, trained on data from historical fires, to allow the propagation of wildfire through the WUI in the model. Historical wildfire ignition locations and NOAA’s hourly time series of surface weather at 2.5 km resolution are used to drive ELMFIRE to produce wildfire hazards representative of the 2022 and 2052 conditions at 30 m resolution, with the future weather conditions scaled to the IPCC CMIP5 RCP4.5 model ensemble predictions. Winds and vegetation were held constant between the 2022 and 2052 simulations, and climate change’s impacts on the future fuel conditions are the main contributors to the changes observed in the 2052 results. Non-zero wildfire exposure is estimated for 71.8 million out of 140 million properties across CONUS. Climate change impacts add another 11% properties to this non-zero exposure class over the next 30 years, with much of this change observed in the forested areas east of the Mississippi River. “Major” aggregate wildfire exposure of greater than 6% over the 30-year analysis period from 2022 to 2052 is estimated for 10.2 million properties. The FSF-WFM represents a notable contribution to the ability to produce property-specific, climate-adjusted wildfire risk assessments in the US.

Climate adjusted projections of the distribution and frequency of poor air quality days for the contiguous United States

“…As a result, climate chemistry model output of wildfire PM2.5 can differ significantly from ground observations. Therefore in our modeling approach, with a focus on proper attribution of wildfire PM2.5, the daily gridded wildfire smoke PM2.5 concentration dataset from Childs et al (2022) is used in combination with simulated ELMFIRE fire emissions (Kearns et al, 2022;Melecio-Vázquez et al, 2023) to characterize current and future impacts to air quality conditions for CONUS due to PM2.5. The Childs et al (2022) data, hereafter "Childs data", which are 10.3389/feart.2024.1320170 openly available via Github, were produced using a machine learning (ML) model that combines ground, satellite, and reanalysis data sources to arrive at PM2.5 concentration estimates.…”

“…Conversely, increased wildfire activity-which currently represents approximately 15%-30% of PM2.5 concentrations in the United States-is likely to significantly impact PM2.5 production into the future (Jacob and Winner, 2009;Spracklen et al, 2009;Liu et al, 2021) and put at risk any improvements gained from controls on anthropogenic emissions. The importance of this point is underscored given the fact that wildfire is the fastestgrowing natural disaster associated with climate change and is expected to continue to increase over the next 30 years (Kearns et al, 2022). While not a direct correlation, fire emission increases of 50% more by mid-century are projected for CONUS in multiple studies (Spracklen et al, 2009;Val Martin et al, 2015;Ford et al, 2018).…”

“…These results are supplemented with EPA air quality monitoring station observations for bias correction and wildfire-specific concentrations from a machine learning model from Childs et al (2022). Ozone results are climate-adjusted using previously published methodology in Wilson et al (2022), and smoke results are climate-adjusted using simulated fires' output from the First Street Foundation Wildfire Model (Kearns et al, 2022). The integrated modeling approach provides a consistent set of projections for the number days for both the current conditions and for projected conditions 30 years into the future reaching an "unhealthy for sensitive groups" (orange colored) or above threshold on the EPA Air Quality Index.…”

Potential water‐related risks to the electric power industry associated with changing surface water conditions

“…Recent publications on SLR focus on coastal infrastructure vulnerability, such as risk to airports (Yesudian & Dawson, 2021), port operations (Christodoulou et al, 2019), and nuclear power plants (Kopytko & Perkins, 2011). The First Street Foundation has recently been publishing studies focusing on climate change and increased flood risk, wildfire risk, and the correlated flood/fire risk facing American communities and associated economic damages (Armal et al, 2020; Kearns et al, 2022; Porter et al, 2021).…”

Disruptions from Wildfire Smoke: Vulnerabilities in Local Economies and Disadvantaged Communities in the U.S.

“…Housing-vulnerable household-days of heavy smoke were largest in California, Illinois, Washington, and New York. Wildfires and wildfire smoke present difficult recovery trajectories for housingvulnerable communities because of the enormous destruction of housing supply, which increases housing costs and exacerbates the nation's housing affordability crisis, especially in the high-demand regions of the West (Kearns 2022;Ibarraran and Ruth 2009). Nearly 72 million properties face some risk of wildfires over the next 30 years, representing an immense challenge to future housing security (First Street Foundation 2022).…”