Disaster recovery spending for major flood events in the United States is at an all-time high. Yet research examining equity in disaster assistance increasingly shows that recovery funding underserves vulnerable populations. Based on a review of academic and grey literature, this article synthesizes empirical knowledge of population disparities in access to flood disaster assistance and outcomes during disaster recovery. The results identify renters, low-income households, and racial and ethnic minorities as populations that most face barriers accessing federal assistance and experience adverse recovery outcomes. The analysis explores the drivers of these inequities and concludes with a focus on the performance of disaster programs in addressing unmet needs, recognition of intersectional social vulnerabilities in recovery analysis, and gaps in data availability and transparency.

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.

Surface air temperature is an important variable in quantifying extreme heat, but high-resolution temporal and spatial measurement is limited by sparse climate-data stations. As a result, hyperlocal models of extreme heat involve intensive physical data collection efforts or analyze satellite-derived land-surface temperature instead. We developed a geostatistical model that integrates in situ climate-quality temperature records, gridded temperature data, land-surface temperature estimates, and spatially consistent covariates to predict monthly averaged daily maximum surface-air temperatures at spatial resolutions up to 30 m. We trained and validated the model using data from North Carolina. The fitted model showed strong predictive performance with a mean absolute error of 1.61 ${}^{\circ }$F across all summer months and a correlation coefficient of 0.75 against an independent hyperlocal temperature model for the city of Durham. We show that the proposed model framework is highly scalable and capable of producing realistic temperature fields across a variety of physiographic settings, even in areas where no climate-quality data stations are available.

The influx of millions of Syrian refugees into Turkey has rapidly changed the population distribution along the Dead Sea Rift and East Anatolian fault zones. In contrast to other countries in the Middle East where refugees are accommodated in camp environments, the majority of displaced individuals in Turkey are integrated into local cities, towns, and villages – placing stress on urban settings and increasing potential exposure to strong earthquake shaking. Yet displaced populations are often unaccounted for in the census-based population models used in earthquake fatality estimations. This study creates a minimally modeled refugee gridded population model and analyzes its impact on semi-empirical fatality estimations across southeast Turkey. Daytime and nighttime fatality estimates were produced for five fault segments at earthquake magnitudes 5.8, 6.4, and 7.0. Baseline fatality estimates calculated from census-based population estimates for the study area varied in scale from tens to thousands of fatalities, with higher death totals in nighttime scenarios. Refugee fatality estimations were analyzed across 500 semi-random building occupancy distributions. Median fatality estimates for refugee populations added non-negligible contributions to earthquake fatalities at four of five fault locations, increasing total fatality estimates by 7–27 %. These findings communicate the necessity of incorporating refugee statistics into earthquake fatality estimations in southeast Turkey and the ongoing importance of placing environmental hazards in their appropriate regional and temporal context.