Major power outages have risen over the last two decades, largely due to more extreme weather conditions. However, there is a lack of knowledge on the distribution of power outages and its relationship to social vulnerability and co-occurring hazards.
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.
Wildfires are a natural part of the ecosystem in the U.S.. It is vital to classify wildfires using a comprehensive approach that simultaneously considers wildfire activity (the number of wildfires) and burned area. On this basis, the influence of hydrometeorological variables on wildfires can be further analyzed.
Better understanding how fires respond to climate variability is an issue of current interest in light of ongoing climate change. However, evaluating the global-scale temporal variability of fires in response to climate presents a challenge due to the intricate processes at play and the limitation of fire data.
As increasing wildfire activity puts pressure on wildland fire suppression resources both nationally and within the state of California, further development of programs and infrastructure that emphasize preventative fuels treatments, e.g. prescribed burning, is critical for mitigating the impacts of wildfire at large spatial scales.
Background
Climate projections signal longer fire seasons and an increase in the number of dangerous fire weather days for much of the world including Australia.
Aims
Lightning detection and attribution to wildfire ignitions is a critical component of fire management worldwide to both reduce hazards of wildfire to values-at-risk and to enhance the potential for wildland fire to provide resource benefits in fire-adapted ecosystems. We compared two operational ground-based lightning detection networks used by fire managers to identify cloud-to-ground stro
Background
Near-term forecasts of fire danger based on predicted surface weather and fuel dryness are widely used to support the decisions of wildfire managers. The incorporation of synoptic-scale upper-air patterns into predictive models may provide additional value in operational forecasting.
Aims
The 2021 North American wildfire season was marked by record breaking fire-conducive weather and widespread synchronous burning, extreme fire behaviour, smoke and evacuations. Relative to 1979–2021, the greatest number of temperature and vapor pressure deficit records were broken in 2021, and in July alone, 3.2 million hectares burned in Canada and the United States.
The 2023 wildfire season in Canada was unprecedented in its scale and intensity. Spanning from late April to early November and extending across much of the forested regions of Canada, the season resulted in a record-breaking total area burned of approximately 15 million hectares, over seven times the historic national annual average.