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1. The fastest-growing and most destructive fires in the US (2001 to 2020)

   https://doi.org/10.1126/science.adk5737  

   Balch, Jennifer K; Iglesias, Virginia; Mahood, Adam L; Cook, Maxwell C; Amaral, Cibele; DeCastro, Amy; Leyk, Stefan; McIntosh, Tyler L; Nagy, R Chelsea; St_Denis, Lise; et al (October 2024, Science)

   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. 

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2. Select CPT-Based Liquefaction Case Histories from the 2001 Nisqually, Washington, Earthquake

   https://doi.org/10.17603/ds2-nsf8-7944  

   Rasanen, Ryan; Geyin, Mertcan; Maurer, Brett (January 2022, Designsafe-CI)

   While soil liquefaction is common in earthquakes, the case history data required to train and test state-of-practice prediction models remains comparatively scarce, owing to the breadth and expense of data that comprise a single case history. The 2001 Nisqually, Washington, earthquake, for example, occurred in a metropolitan region and induced damaging liquefaction in the urban cores of Seattle and Olympia, yet case history data has not previously been published. Accordingly, we compile 24 cone-penetration-test (CPT) case histories from free-field locations. The many methods used to obtain and process the data are detailed in the accompanying manuscript, as is the structure of the digital dataset. The case histories are then analyzed by 18 existing liquefaction response models to determine whether any is better, and to compare model performance in Nisqually against global observations. While differences are measured, both between models and against prior global case histories, these differences are often statistically insignificant considering finite-sample uncertainty. This alludes to the general impropriety of championing models based on individual earthquakes or otherwise small datasets, and to the ongoing need for additional case history data and more rigorous adherence to best practices in model training and testing. 

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3. Siberian taiga and tundra fire regimes from 2001–2020

   https://doi.org/10.1088/1748-9326/ac3f07  

   Talucci, Anna C.; Loranty, Michael M.; Alexander, Heather D. (January 2022, Environmental Research Letters)

   Abstract Circum-boreal and -tundra systems are crucial carbon pools that are experiencing amplified warming and are at risk of increasing wildfire activity. Changes in wildfire activity have broad implications for vegetation dynamics, underlying permafrost soils, and ultimately, carbon cycling. However, understanding wildfire effects on biophysical processes across eastern Siberian taiga and tundra remains challenging because of the lack of an easily accessible annual fire perimeter database and underestimation of area burned by MODIS satellite imagery. To better understand wildfire dynamics over the last 20 years in this region, we mapped area burned, generated a fire perimeter database, and characterized fire regimes across eight ecozones spanning 7.8 million km²of eastern Siberian taiga and tundra from ∼61–72.5° N and 100° E–176° W using long-term satellite data from Landsat, processed via Google Earth Engine. We generated composite images for the annual growing season (May–September), which allowed mitigation of missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. The annual dNBR images were converted to binary burned or unburned imagery that was used to vectorize fire perimeters. We mapped 22 091 fires burning 152 million hectares (Mha) over 20 years. Although 2003 was the largest fire year on record, 2020 was an exceptional fire year for four of the northeastern ecozones resulting in substantial increases in fire activity above the Arctic Circle. Increases in fire extent, severity, and frequency with continued climate warming will impact vegetation and permafrost dynamics with increased likelihood of irreversible permafrost thaw that leads to increased carbon release and/or conversion of forest to shrublands. 

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4. More Trees, More Poverty? The Socioeconomic Effects of Tree Plantations in Chile, 2001–2011

   https://doi.org/10.1007/s00267-015-0594-x  

   Andersson, Krister; Lawrence, Duncan; Zavaleta, Jennifer; Guariguata, Manuel R. (August 2015, Environmental Management)

   Abstract Tree plantations play a controversial role in many nations’ efforts to balance goals for economic development, ecological conservation, and social justice. This paper seeks to contribute to this debate by analyzing the socioeconomic impact of such plantations. We focus our study on Chile, a country that has experienced extraordinary growth of industrial tree plantations. Our analysis draws on a unique dataset with longitudinal observations collected in 180 municipal territories during 2001–2011. Employing panel data regression techniques, we find that growth in plantation area is associated with higher than average rates of poverty during this period. 

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5. North Temperate Lakes LTER: Pelagic Prey - Sonar Data 2001 - current

   https://doi.org/10.6073/pasta/5eacf039425717bc9bc766962489e184  

   Magnuson, John; Carpenter, Stephen; Stanley, Emily (January 2022, Environmental Data Initiative)

   Total pelagic fish abundance data were collected annually in mid-summer using sonar along a set of transects in each of eight lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, Mendota, Monona, and Fish), from 1981-1999, and in Lakes Monona and Fish from 1995-1999. This data is not available online (contact gahler@wisc.edu). No data was collected in 2000. In 2001, collection resumed on Crystal, Sparkling, and Trout. In 2005, collection resumed on Lake Mendota. This data is included in this dataset as CSV files. The data represent lake-wide density estimates for abundant pelagic prey species in each lake. The sampling on each lake was conducted in depths greater than 5 meters to avoid hazards to equipment. In addition, because of the near field acoustic effects, the upper 2 meters of the water column is not represented in the data. Although they were rare, large targets representing predatory species were excluded from the density estimation for pelagic prey species using the proportion of large targets identified during single target analysis on each lake. Densities for Sparkling, Crystal and Mendota are for the entire basin of each lake. The data shown for Trout Lake represent densities in only the south basin. Number of sites: 4 

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