Skip to main content

Tyson Ochsner

Advancements in wildfire danger modeling may increase wildfire preparedness, and therefore decrease loss of life, property, and habitat due to wildfire. Recent work by our team has shown wildfire danger models may be improved by incorporating soil moisture information. Still, soil moisture—an important determinant of wildfire risk—is not currently used for wildfire danger assessments because adequate soil moisture information has historically been unavailable. Our project addressed this gap by developing and disseminating improved soil moisture estimates and demonstrating their relevance to wildfire danger assessments. Our objectives were to (1) develop an effective model of soil moisture for the Red River and Rio...
Categories: Publication; Types: Citation
thumbnail
Megafire Risk Evaluation System (MERES) for the Southern Great Plains
Characterized by their extreme size, intensity, and severity, megafires are the most destructive, dangerous, and costly wildfires in the U.S. Over the past two decades, megafires have become more frequent in Oklahoma and Texas along with increasing extreme weather events and changes to fuel types caused by woody plant encroachment into grasslands. As climate change and woody plant encroachment are expected to continue or even accelerate, it is important to evaluate megafire risks and locate high-risk areas. This project will develop a new Megafire Risk Evaluation System (MERES) and make future projections of megafire probability in Oklahoma and Texas from 2024 to 2100. Outcomes and products from this project will...
thumbnail
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...
Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass-yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county-level wild hay yield reported by...
Categories: Publication; Types: Citation
Researchers developed a custom model that integrates gSSURGO soil property data with condensed climate data from PRISM (e.g., drought index) to predict fraction of available water for a given soil. The model was trained with in situ measured soil moisture data (point measurements) and expanded to spatial extent with gSSURGO maps and PRISM data. The code for the model was developed using a combination of statistical and GIS languages (R, Matlab, ArcGIS, etc.).
View more...
ScienceBase brings together the best information it can find about USGS researchers and offices to show connections to publications, projects, and data. We are still working to improve this process and information is by no means complete. If you don't see everything you know is associated with you, a colleague, or your office, please be patient while we work to connect the dots. Feel free to contact sciencebase@usgs.gov.