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Abstract In the Western US, area burned and fire size have increased due to the influences of climate change, long-term fire suppression leading to higher fuel loads, and increased ignitions. However, evidence is less conclusive about increases in fire severity within these growing wildfire extents. Fires burn unevenly across landscapes, leaving islands of unburned or less impacted areas, known as fire refugia. Fire refugia may enhance post-fire ecosystem function and biodiversity by providing refuge to species and functioning as seed sources after fires. In this study, we evaluated whether the proportion and pattern of fire refugia within fire events have changed over time and across ecoregions. To do so, we processed all available Landsat 4–9 satellite imagery to identify fire refugia within the boundaries of large wildfires (405 ha+) in 16 forested ecoregions of the Western US. We found a significant change in % refugia from 1986–2021 only in one ecoregion—% refugia increased within fires in the Arizona/New Mexico Mountain ecoregion (AZ/NM). Excluding AZ/NM, we found no significant change in % refugia across the study area. Furthermore, we found no significant change in mean refugia patch size, patch density, or mean distance to refugia. As fire size increased, the amount of refugia increased proportionally. Evidence suggests that fires in AZ/NM had a higher proportion of reburns and, unlike the 15 other ecoregions, fires did not occur at higher elevation or within greener areas. We suggest several possibilities for why, with the exception of AZ/NM, ecoregions did not experience a significant change in the proportion and pattern of refugia. In summary, while area burned has increased over the past four decades, there are substantial and consistent patterns of refugia that could support post-fire recovery dependent on their spatial patterns and ability to function as seeds sources for neighboring burned patches. 

The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across the contiguous US. Nearly half of the ecoregions experienced destructive fast fires that grew more than 1620 hectares in 1 day. These fires accounted for 78% of structures destroyed and 61% of suppression costs ($18.9 billion). From 2001 to 2020, the average peak daily growth rate for these fires more than doubled (+249% relative to 2001) in the Western US. Nearly 3 million structures were within 4 kilometers of a fast fire during this period across the US. Given recent devastating wildfires, understanding fast fires is crucial for improving firefighting strategies and community preparedness. 

Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline.