Large, hot, fast-moving wildfires are an increasing threat to communities and to the watersheds they rely upon. Forest managers work to reduce the risk from these fires using forest thinning and prescribed burning to reduce the amount of fuel in the forest. However, these activities are expensive, and it can be challenging to identify which acres of forest should be targeted with thinning and burning treatments.
The goal of this research is to improve an existing modeling tool that helps forest managers find the ‘right acres’ to target with treatments to help to reduce wildfire risk. Focusing on the Santa Fe Fireshed, researchers will first use different numbers of forest inventory plots to estimate how the forest vegetation changes over short distances and how those changes alter the way that simulated wildfire behaves. Measuring this variability will allow researchers to determine how many inventory plots are needed in order for the model to accurately simulate the landscape. Once the initial vegetation layer represents the forest conditions, researchers will simulate how future climate change and hotter, drier conditions will cause fire to spread--information which can be used to identify which forests are most at risk of experiencing high-severity fires. The model will then be used to simulate the extent to which management actions such as thinning and prescribed burning change that risk.
This project will result in an improved tool for enabling forest managers to better target the placement of forest treatments aimed at reducing wildfire risk. This research will be conducted in the Santa Fe Fireshed in the Rio Grande watershed, but the resulting modeling approach will be useful for planning beyond the study area.
