Wildfire Probability Mapping Based on Regional Soil Moisture Models
Wildfires scorched 10 million acres across the United States in 2015, and for the first time on record, wildfire suppression costs topped $2 billion. Wildfire danger modeling is an important tool for understanding when and where wildfires will occur, and recent work by our team in the South Central United States has shown wildfire danger models may be improved by incorporating soil moisture information. Advancements in wildfire danger modeling may increase wildfire preparedness, and therefore decrease loss of life, property, and habitat due to wildfire. Still, soil moisture—an important determinant of wildfire risk—is not currently used for wildfire danger assessments because data are generally unavailable at the appropriate scales of space and time.
Our project addresses this knowledge gap by developing and disseminating improved wildfire danger assessments that are rooted in high precision estimates of soil moisture. Primary goals are (1) develop an effective model of soil moisture for the Red River and Rio Grande basins using soil maps and climate data; (2) quantify the relationships between modeled soil moisture and wildfire probability; and (3) distribute soil moisture and wildfire probability maps for both basins. Primary outcomes of this work include new web-based tools for exploring soil moisture dynamics in near real-time and relating those dynamics to wildfire probability. We will initiate an aggressive outreach program to ensure our results will have practical applications relevant to a wide range of stakeholders interested in drought, flooding, and fire monitoring and prediction. The intended users of research outcomes include hydrologists, soil scientists, fire planners, land management personnel from universities, state and federal agencies and stakeholder groups including landscape conservation cooperatives and Tribal organizations. In addition to using soil moisture data for wildfire probability models, the maps will be applicable to decisions for planning prescribed fire treatments and post-fire reclamation activities.
- Source: USGS Sciencebase (id: 594aa23de4b062508e36f43c)
Wildfires scorched 10 million acres across the United States in 2015, and for the first time on record, wildfire suppression costs topped $2 billion. Wildfire danger modeling is an important tool for understanding when and where wildfires will occur, and recent work by our team in the South Central United States has shown wildfire danger models may be improved by incorporating soil moisture information. Advancements in wildfire danger modeling may increase wildfire preparedness, and therefore decrease loss of life, property, and habitat due to wildfire. Still, soil moisture—an important determinant of wildfire risk—is not currently used for wildfire danger assessments because data are generally unavailable at the appropriate scales of space and time.
Our project addresses this knowledge gap by developing and disseminating improved wildfire danger assessments that are rooted in high precision estimates of soil moisture. Primary goals are (1) develop an effective model of soil moisture for the Red River and Rio Grande basins using soil maps and climate data; (2) quantify the relationships between modeled soil moisture and wildfire probability; and (3) distribute soil moisture and wildfire probability maps for both basins. Primary outcomes of this work include new web-based tools for exploring soil moisture dynamics in near real-time and relating those dynamics to wildfire probability. We will initiate an aggressive outreach program to ensure our results will have practical applications relevant to a wide range of stakeholders interested in drought, flooding, and fire monitoring and prediction. The intended users of research outcomes include hydrologists, soil scientists, fire planners, land management personnel from universities, state and federal agencies and stakeholder groups including landscape conservation cooperatives and Tribal organizations. In addition to using soil moisture data for wildfire probability models, the maps will be applicable to decisions for planning prescribed fire treatments and post-fire reclamation activities.
- Source: USGS Sciencebase (id: 594aa23de4b062508e36f43c)