Assessing risks to multiple resources affected by wildfire and forest management using an integrated probabilistic frameworkThis article is part of a larger document. View the larger document here.
The tradeoffs that surround forest management are inherently complex, often involving multiple temporal and spatial scales. For example, conflicts may result when fuel treatments are designed to mediate long-term fuel hazards, but activities could impair sensitive aquatic habitat or degrade wildlife habitat in the short term. This complexity makes it hard for managers to describe and communicate the conditional nature of risk and to justify planned activities to stakeholders. In addition, our understanding of how proposed activities will affect resources of concern is often limited owing to informational shortcomings and imprecise models. To be robust and transparent, a risk assessment framework needs to reveal these limitations while quantifying the probable outcomes of project effects to multiple resources of concern. In this analysis, we describe the effects of fuel treatments using such a planning framework called CRAFT (Comparative Risk Assessment Framework and Tools). CRAFT provides a platform from which diverse ancillary models and other relevant information can be transparently integrated and evaluated. We conducted our case study in the southwestern Klamath Mountains of California. As is typical of most montane forests of California, this area has experienced decades of fire suppression, and severe effects from wildfire are a concern. Working with managers, we identified a range of measurable objectives involving the wildland urban interface, fire behavior, fire effects, and sensitive wildlife. We then developed a conceptual model describing how components of the system interrelate. From this, we developed a probabilistic framework, using Bayesian belief networks, in which we employed existing fire models to address how expected fire behavior varies across different burning scenarios. Our framework provides decisionmakers and stakeholders with insights into the condition probability that management alternatives will be successful.