Improving long-term fuel treatment effectiveness in the National Forest System through quantitative prioritization

Predicting the efficacy of fuel treatments aimed at reducing high severity fire in dry-mixed conifer forests in the western US is a challenging problem that has been addressed in a variety of ways using both field observations and wildfire simulation models. One way to describe the efficacy of fuel treatments is to quantify how often wildfires are expected to intersect areas prioritized for treatment. In real landscapes treatments are static, restricted to a small portion of the landscape and against a background of stochastic fire and dynamic vegetation, thus the likelihood of fire encountering a treatment during the period treatments remain effective is small. In this paper we simulate a wide range of different treatment prioritization schemes using the forest landscape simulation model Envision to examine 50 years of fire-treatment interactions and forest succession. We first reviewed 47 fuel management projects in Oregon, USA to build prioritization schemes that addressed different fuel management objectives. We then simulated different priority schemes in the 18 planning areas of the Deschutes National Forest in central Oregon and measured potential fire-treatment interactions over time. Simulated annual area burned was used to calculate the success odds for each priority scheme and planning area. Out of the ten metrics considered only three had higher success odds than a random prioritization of planning areas. Spatial allocation of projects based on burn probability and transmitted wildfire had the highest success odds among the tested metrics. However, success odds declined sharply as desired success levels increased suggesting that fuel management goals need to be tempered to consider the stochastic nature of wildfire. Meeting long-term multiple management goals over time can benefit from consideration of short- and long-term tradeoffs from different treatment prioritization schemes. Our work contributes towards a better framing of both management and public expectations regarding the performance of fuel treatments programs.