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.
In the United States, flood events are the most economically damaging type of natural disaster. Some of the most widely used tools for understanding property flood risk in the United States are the Flood Insurance Rate Maps (FIRMs) produced by the Federal Emergency Management Agency (FEMA). Numerous previous studies have attempted to estimate the impact on property valuation from a home’s being mapped into a Special Flood Hazard Area (SFHA) within FIRMs. However, as these maps have widely served as the source of data about true flood risk, there have been limits on the ability of researchers to disentangle these zone designation impacts as due to actual flood risk or as due to perceived flood risk. New advancements in flood modeling have allowed for the prediction of high-quality property-level flood inundation, both now and in the future. By integrating these flood modeling advancements, true flood risk may be controlled for in models looking to explore the avenues by which property valuation impacts occur. To this end, this study builds on insights from recent research looking at the valuation of single-family residential properties in Miami-Dade County (MDC), which utilizes a high-resolution floodplain model to estimate the impact of actual property inundation on sales prices. By controlling for actual property flood risk, impacts of SFHA designations are estimated in MDC through implementation of a difference in difference model which utilizes the release of updated FIRMS in 2009 and the 217,222 transactions and 120,693 property designation changes which occurred within the dataset.
Changing environmental conditions are driving worsening flood events, with consequences for counties, cities, towns, and local communities. To understand individual flood risk within this changing climate, local community resiliency and infrastructure impacts must also be considered. Past research has attempted to capture this but has faced several limitations. This study provides a nation-wide model of community flooding impacts within the United States currently and in 30 years through the use of high-resolution input data (parcel-level), multi-source flood hazard information (four major flood types), multi-return period hazard information (six return periods), operational threshold integration, and future-facing projections. Impacts are quantified here as the level of flooding relative to operational thresholds. This study finds that over the next 30 years, millions of additional properties will be impacted, as aspects of risk are expected to increase for residential properties by 10%, roads by 3%, commercial properties by 7%, critical infrastructure facilities by 6%, and social infrastructure facilities by 9%. Additionally, certain counties and cities persistently display impact patterns. A high-resolution model capturing aspects of flood risk as related to community infrastructure is important for an understanding of overall community risk.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.