For thousands of years, fire has been a critical tool in land management, from the prehistoric use of controlled burns around Stonehenge to modern-day fire practices.

Early humans recognized the importance of fire in maintaining healthy ecosystems – clearing the underbrush, enriching soil, and supporting biodiversity. This ancient practice, now called prescribed burning, remains essential to preventing the large-scale, destructive wildfires we increasingly face today.

Unfortunately, the use of prescribed burns has plummeted over the last century. Urban expansion, stricter environmental regulations, and the growing threat of climate change have all contributed to this decline. In areas where towns and cities encroach on natural landscapes - what we now call the wildland-urban interface (WUI) - the risks associated with prescribed burns have risen. As a result, the windows of opportunity for safely conducting these burns are narrowing. Even in years with favorable conditions, fire agencies often find it difficult to locate enough suitable times for prescribed fires due to unpredictable weather patterns and shifting environmental changes.

This growing challenge highlights the need for advanced predictive tools in managing controlled, low-intensity burns. Given the limited windows for prescribed fires, we must act quickly and precisely. Near human habitation, fire managers can no longer rely on guesswork; they need accurate predictions of fire behavior. It’s not just about choosing the right day - it’s about understanding how variables like wind, humidity, and, critically, fuel moisture influence fire intensity and spread.

Fuel moisture - the dampness of leaves, trees, and underbrush, plays a pivotal role in fire behavior and can change rapidly, even within hours. Accurately predicting these moisture levels is crucial for managing prescribed burns, as small fluctuations can dramatically alter how a fire behaves. This is why real-time models that simulate changing conditions are more important than ever. They allow fire managers to fine-tune their strategies and prevent prescribed burns from escalating into uncontrollable wildfires.

Our recent work focuses on developing a new physics-based model that predicts fuel moisture variation and its impact on fire behavior with unprecedented accuracy. This model takes into account the complex dynamics of fuel drying and fire spread, refining our ability to accurately predict low-intensity fire behavior and offering a much-needed tool for fire managers. By incorporating detailed data on local weather, fuel properties, and topography, we can better predict the safest windows for prescribed burns, even in environments where conditions change rapidly.

These advancements in fire prediction models are key to restoring public trust in prescribed burns. With more accurate forecasts, fire agencies can confidently carry out these controlled burns, knowing they can prevent them from spiraling out of control. This, in turn, protects both ecosystems and nearby communities from the devastation of larger, unmanageable wildfires.

By reintroducing prescribed burns in a safer, more scientifically informed way, we can return to an ancient practice that supports healthy landscapes. The future of wildfire prevention may lie in a return to the past—but this time, with cutting-edge precision.