|Title||Rating fire danger from the ground up|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Levi MR, Krueger E, Snitker GJ, Ochsner T, Villarreal ML, Elias EH, Peck DE|
|ARIS Log Number||369205|
Better soil moisture information with spatial and temporal resolutions relevant for assessing fuel bed conditions and wildfire probability offers promise for improving fire danger ratings. Wildfires consumed more than 3.5 million hectares in the United States in 2018 and federal fire suppression costs topped $3 billion USD. These fires took a severe toll on many communities, resulting in the destruction of more than 18,000 residences and the deaths of more than 100 people. Wildfire damages like these are a threat in many nations, and researchers from across the globe, and across multiple scientific disciplines, are working to improve fire danger rating systems to help protect natural resources and human health and safety. One new concept emerging as a valuable contribution to this effort is the integration of soil moisture information as a predictor of wildfire probability. Soil moisture, particularly within the root zone, is a key link between weather conditions, such as precipitation and temperature, and the characteristics of the live vegetative “fuel bed”, such as fuel moisture and fuel loads. These dynamic vegetation characteristics strongly influence wildfire probability and can be challenging to model and monitor at relevant spatial and temporal scales using field data or optical remote sensing of fuel moisture (e.g., remote sensing live fuel moisture models contain relatively large margins of error that varies by vegetation type). Soil moisture monitoring capabilities, in contrast, have been steadily growing due to the development of in situ networks and dedicated satellites. While the relationships between soil moisture, fuel conditions, and wildfire occurrence have long been intuitively understood, the increasing availability of soil moisture information is creating significant opportunities to quantify these relationships and incorporate them into new or existing weather-based fuel moisture models and fire danger rating systems.