Downstream protection value: Detecting critical zones for effective fuel-treatment under wildfire risk

Abstract

The destructive potential of wildfires has been exacerbated by climate change, causing their frequencies and intensities to continuously increase globally. Generating fire-resilient landscapes via efficient and calculated fuel-treatment plans is critical to protecting native forests, agricultural resources, biodiversity, and human communities. To tackle this challenge, we propose a framework that integrates fire spread, optimization, and simulation models. We introduce the concept of Downstream Protection Value (DPV), a flexible metric that assays and ranks the impact of treating a unit of the landscape, by modeling a forest as a network and the fire propagation as a tree graph. Using our open-source decision support system, custom performance metrics can be optimized to minimize wildfire losses, obtaining effective treatment plans. Experiments with real forests show that our model is able to consistently outperform alternative methods and accurately detect high-risk and potential ignition areas, focusing the treatment on the most critical zones. Results indicate that our methodology is able to decrease the expected area burned and fire propagation rate by more than half in comparison to alternative methods under ignition and weather uncertainty.