During drought, resource managers want to know when the drought will end to make informed management decisions. However, as anthropogenic climate change has intensified drought conditions, we hypothesize it has affected drought recovery.

Wildfires in the southwestern United States are increasingly frequent and severe, but whether these trends exceed historical norms remains contested. Here we combine dendroecological records, satellite-derived burn severity, and field measured tree mortality to compare historical (1700-1880) and contemporary (1985-2020) fire regimes at tree-ring fire-scar sites in Arizona and New Mexico.

Large-scale wildfires are becoming increasingly common in the wet forests of the Pacific Northwest (USA), with predicted increases in fire prevalence under future climate scenarios. Wildfires can alter streamflow response to precipitation and mobilize water quality constituents, which pose a risk to aquatic ecosystems and downstream drinking water treatment.

The Yurok Tribe, along with other tribal communities in northwest California, non-profit organizations, universities, and governmental agencies are working to restore forests and woodlands to be more resilient to wildfires, drought, pests and diseases.

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world.

Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite-based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales.

Wildfires in the western US are increasing in frequency, size, and severity. These disturbances alter soil microbiome structure and function, with greater fire severity leading to more pronounced impacts to bacterial, archaeal, and fungal communities.

Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms.

Fire agencies across the United States must make complex resource allocation decisions to manage wildfires using a national network of shared firefighting resources. Firefighters play a critical role in suppressing fires and protecting vulnerable communities. However, they are exposed to health and safety risks associated with fire, smoke inhalation, and infectious disease transmission.

Projected increases in wildfire frequency, size, and severity may further stress already scarce firefighting resources in the western United States that are in high demand. Machine learning is a promising field with the ability to model firefighting resource usage without compromising dataset size or complexity.