A Hierarchical Framework for Quantifying Seasonal and Daily Wildland Fire Risk in Great Plains Grasslands

Accurate quantification of wildfire risk is essential for balancing wildfire mitigation and prescribed fire management in grassland ecosystems, yet existing fire danger indices do not explicitly distinguish seasonal fuel dynamics from daily weather variability. This study presents a hierarchical framework for quantifying wildland fire risk by explicitly separating seasonal wildfire potential from daily weather-driven fire activity. The framework introduces the Daily Burned Area Ratio (DBAR) as a quantitative measure of realized wildfire risk and decomposes it into the Seasonal Burned Area Ratio (SBAR) and the Daily Burn Activity Index (DBAI). Wildfire records from the U.S. Forest Service Fire Program Analysis Fire-Occurrence Database, Oklahoma Mesonet weather observations, and remotely sensed vegetation data collected between 1995 and 2020 were used to develop and evaluate the framework in the Flint Hills of Kansas and Oklahoma. SBAR was modeled using grass curing and air temperature to characterize the seasonal baseline of wildfire activity, whereas DBAI was modeled using dead fuel moisture content (DFMC) and wind speed to quantify day-to-day departures from that seasonal baseline. The SBAR model accurately reproduced the characteristic bimodal wildfire regime of the Great Plains, whereas the DBAI model identified DFMC as the dominant control on daily wildfire activity, with wind speed providing an important secondary influence. Compared with the Burning Index (BI) and the Grassland Fire Danger Index (GFDI), the hierarchical framework achieved superior performance in discriminating fire days from non-fire days. Global sensitivity analysis further demonstrated that the framework provides a more balanced representation of the influences of grass curing, relative humidity, air temperature, and wind speed than the conventional indices. By explicitly separating seasonal fuel dynamics from short-term weather variability, the proposed framework provides an ecologically interpretable, locally calibratable, and operationally practical approach to wildfire risk assessment. Because the seasonal and daily components can be calibrated independently, the framework is readily transferable to other grassland ecosystems and provides a flexible foundation for adaptive wildfire and prescribed fire management under changing climatic conditions.