decision making

We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions.

Significant resources have been devoted to increasing the supply of data and information products for wildland fire management. There has been comparatively less emphasis on understanding the demand for these products. There are large differences in the number of information sources that fire managers use in decision making.

Environmental governance outcomes hinge on the design and implementation of management decisions. Yet, available research methodologies can be limited in their ability to capture the complexity of decision-making and implementation processes and in turn predict and explain environmental governance outcomes.

Aim

Background

Public land management agencies in the US are committed to using science-informed decision making, but there has been little research on the types and topics of science that managers need most to inform their decisions.

In the current century, wildfires have shown an increasing trend, causing a huge amount of direct and indirect losses in society. Different methods and efforts have been employed to reduce the frequency and intensity of the damages, one of which is implementing prescribed fires. Previous works have established that prescribed fires are effective at reducing the damage caused by wildfires.