Brooklyn (NY) — First Street Foundation today released the First Street Foundation Wildfire Model, the only nationwide, probabilistic, climate adjusted, peer reviewed, property specific wildfire risk model for properties in the contiguous United States. Detailed in the 5th National Risk Assessment: Fueling the Flames, the model provides a first of its kind analysis of the risk individual properties face from damaging wildfires today, and up to 30 years in the future as a result of climate changes.
Nationwide, the report finds nearly 20 million properties face “Moderate” risk, (up to a 6% chance of experiencing a wildfire over 30 years); 6 million properties face “Major” risk (up to 14% risk over 30 years); nearly 3 million face “Severe” risk (up to 26% over 30 years); and approximately 1.5 million face “Extreme” risk (greater than 26% risk over 30 years). Over 49 million properties face less than 1% chance of experiencing a wildfire over a 30-year period, or “Minor” risk in the model.
Wildfire has become one of the most common and dangerous climate perils, increasingly spreading from heavily forested areas to more populous urban and suburban environments. According to NOAA, damage associated with wildfires has grown substantially, with $81.7 billion, or 66% of all direct losses since 1980, occurring in the last five years. Yet today, neither the public nor private sector have developed a simple methodology or tool to help homeowners, buyers or renters understand a property’s wildfire risk, and make informed decisions to protect them.
Existing tools like USDA Forest Service’s wildfire risk assessment are designed to help fire officials understand how risk varies across a state, region, or county; it is explicitly not meant to help homeowners understand their personal risk. To address this gap, First Street Foundation will make this critical wildfire risk information available to users for free through Risk Factor™, where Fire Factor™ data will be presented alongside Flood Factor® and other future perils, giving users a comprehensive understanding of their homes from physical climate risk today and 30 years into the future. Like Flood Factor, Fire Factor data will be integrated into Realtor.com®, providing visitors to the site a property-level wildfire risk assessment in the form of a risk ranking from 1 (Minimal) to 10 (Extreme) for each property on the site. Users interested in commercial real estate can also find this data integrated with Crexi®.
“The lack of a property specific, climate adjusted wildfire risk for individual properties has severely hindered everyone from the federal government to your average American,” said Matthew Eby, Founder and Executive Director of First Street Foundation. “As a changing climate drives more frequent and severe wildfire events, Fire Factor will prove critical in ensuring everyone has the insights they need to understand their personal risk to avoid and protect against the devastating impact of a wildfire.”
“According to a recent Realtor.com® survey, seven out of ten recent homebuyers considered the risk of natural disasters when deciding where to live. Realtor.com® is adding Fire Factor to maps and properties to help home shoppers and homeowners make informed decisions,” said Sara Brinton, Lead Product Manager, Realtor.com®. “Wildfire risk information empowers consumers to protect their homes against the increasing threat of wildfire damage.”
Building the model brought together top climate and data scientists, technologists, and modelers from other leading organizations; the Spatial Informatics Group, Reax Engineering, and Eagle Rock Analytics who are members of the Pyregrence Consortium as well as the USGS, and architectural design & engineering consulting group Arup. This group combined decades of peer reviewed research and expertise in next-generation modeling techniques to create an open source, freely available wildfire model that accounts for current and future climate conditions.
First Street Foundation is a nonprofit 501(c)(3) research and technology group working to define America’s growing climate risk.
Article courtesy of First Street Foundation.
More highlights below.
The First Street Foundation has expanded its portfolio of peer reviewed, property specific, climate adjusted physical risk models with the launch of the First Street Foundation Wildfire Model, estimating the risk of wildfire on a property-by-property basis across the United States today, and up to 30 years into the future. This high-precision, climate-adjusted wildfire model provides insights for individual property owners of residential, commercial, critical, and social infrastructure buildings. These results are made available through Risk FactorT™, the first free source of high-quality probabilistic wildfire risk information at the property level available to the public.
The model was developed in partnership with researchers and wildfire experts from First Street Foundation and the Pyregence consortium, including Spatial Informatics Group, Reax Engineering, and Eagle Rock Analytics. This analysis follows the open science approach taken by First Street Foundation for climate-adjusted flood risk.
The First Street Foundation Wildfire Model integrates information on fuels, wildfire weather, and ignition into a Fire Behavior Model. The wildfire model requires data on the combustible fuels which may contribute to wildfire across the United States. The 2016 update, Version 2.0.0, of the canonical U.S. Forest Service (USFS) LANDFIRE (LANDFIRE, 2021) fuels dataset at the 30 meter resolution serves as a baseline of this fuels estimate, and that dataset is updated by including additional information of all known “disturbances” between 2016 and 2020 which could modify or change the fuels in a way not captured in the original dataset. These “disturbances” include activities such as recent wildfires, prescribed burns, harvests, and other forest management practices.
Another important and novel update included in the First Street Foundation Wildfire model is the reclassification of homes and other buildings from a “nonburnable” fuel type to a “burnable” fuel type. Typically, homes and other buildings are classified as nonburnable fuel types within LANDFIRE v2.0.0. In order to allow the wildfire behavior model to more accurately estimate how wildfire moves through the Wildland-Urban-Interface (WUI), properties within the WUI must be replaced by a burnable fuel type so as to not block the modeled wildfire spread.
To represent a wide range of possible weather-driven wildfire conditions across the landscape within the simulations employed here, the model utilizes a decade of NOAA weather data, the 2011–2020 Real Time Mesoscale Analysis (RTMA) dataset (NOAA/NCEP, 2022) augmented by data from Oregon State’s PRISM dataset (Parameter-elevation Regressions on Independent Slopes Model; PRISM, 2021).
These weather data include hourly surface wind, air temperature, relative humidity, and precipitation information at the 2.5 km horizontal resolution. This weather data supports a wide range of possible weather conditions, not to recreate any particular wildfire events, but to drive the wildfire behavior model millions of times in a Monte Carlo simulation scheme to derive 2022 wildfire hazard estimates.
Similarly, for 2052 the same weather time series was used to drive the simulations, but the air temperature, humidity, and precipitation were bias-adjusted to 2052 conditions following the CMIP5 RCP4.5 ensemble results. Rather than applying a bias-adjustment to the wind time series for the future climate, the same winds from the 2011–2020 time series were used to drive the 2052 simulations to reduce sensitivity of the model to highly uncertain future predictions of winds.
One of the primary indicators of where future wildfires will occur is informed through data on historical wildfire occurrences. These historical wildfires help to inform where wildfires may occur vis-à-vis the Fire Occurrence Database (FOD) developed by the USDA Forest Service (Short, 2014; Short, 2021). An open source wildfire behavior model was used, ELMFIRE (Eulerian Level Set Model of Fire Spread). This work does not develop new techniques for wildfire modeling, but rather implements computationally efficient and scalable modeling techniques at a high resolution based on existing science, wildfire probability, and hazard modeling paradigms. These scalable techniques make it practical to more easily conduct wildfire simulations at the 30 meter resolution across the entire country, enabling property and building specific assessments of wildfire risk.
For each 30 meter pixel across the country, information is recorded on the distribution and occurrence of burn incidence, flame lengths experienced, and the relative amount of embers which land in the pixel. These provide estimates of:
- Burn probability: the estimated likelihood of the area burning during any single year.
- Fire intensity: estimated flame lengths, including maximum, average, and sum of all flame lengths experienced.
- Ember exposure: the relative amount of embers which land in an area due to nearby simulated wildfires.
Full article here.
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Featured image via NASA.