Data-driven approach to evaluate fire risk for grassland prescribed burning management in the Great Plains Region

Abstract

Prescribed burning is a critical land management practice in the Great Plains of North America, helping to maintain native rangelands, reduce wildfire risk, and to facilitate favorable grazing. However, the Great Plains experienced approximately 498,000 wildfires between 1992 and 2020 which raised concerns about the effectiveness and safety of prescribed fire as a land management tool. Barriers to prescribed burning practice remain due to concerns on potential fire escape and fire danger. A localized fire danger index can help address these concerns by providing clear, science-based guidance, encouraging safer and confident use of prescribed fire. The Overall goal of this research is to develop a localized Grassland fire danger index through a data-driven modeling approach to support the planning and implementation of prescribed fire in the Great Plains. The specific research objectives of this dissertation include: (1) characterizing wildfire risks across the Great Plains using long-term historical records and climatic data to evaluate the spatial and temporal variability of fire activity and to identify key factors influencing wildfire occurrence; (2) developing user-friendly sub-models for dead fuel moisture content (DFMC) and grass curing, which serve as components of the proposed Grassland Fire Danger Index (GFDI), using localized meteorological and satellite data to represent mid-term and short-term drivers of fire potential. To characterize risks for wildfires risk in the Great Plains, this study analyzes wildfire records from 1992 to 2020 across the Great Plains states to assess spatiotemporal patterns in wildfire risk and evaluate the role of prescribed fires through combined analysis of wildfire data. Results show a threefold increase in both wildfire frequency and area burned, with fire size increasing from east to west and frequency rising from north to south. Wildfire seasons have gradually shifted earlier due to climate change. Drought severity accounted for 51% of the interannual variability in area burned, while grass curing accounted for 60% of the monthly variability of wildfires in grasslands. To develop user-friendly sub-models for DFMC and grass curing, which serve as components of the proposed GFDI, this study uses Oklahoma Mesonet weather data and satellite observations in a data-driven statistical approach. DFMC reflects short-term fuel moisture that affects ignition and fire spread, while grass curing represents seasonal drying that controls fuel availability. Both are critical for fire prediction and safe burns. Lower DFMC and higher grass curing levels are strongly associated with wildfire risks. The DFMC sub-model improves the accuracy and sensitivity of existing models. The grass curing sub-model shows that 50% curing usually occurs around April 15–16, which matches the time for the most intensive prescribed fire activities in the region, indicating it as a safe and effective window for prescribed fire recognized by landowners. The detailed findings from this research will lay a foundation for the development of a localized fire danger assessment tool that integrates long-term, mid-term, and short-term risk factors to improve prescribed fire planning and implementation in the Great Plains. The outcome of this study will assist land managers, fire professionals, and policymakers in making informed decisions for safer and more effective prescribed burning.