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Programmatic Analysis of Fuel Treatments: from the landscape to the national level - Final Report to the Joint Fire Science Program

Year Published: 2018

The importance of cost effective fuel treatment programs has appeared consistently in federal directives (FLAME ACT, National Cohesive Strategy, U.S Department of Interior Office of Policy Analysis) as a priority. Implementing cost effective fuel treatment programs requires a spatially explicit and integrated systematic approach that can be applied to the landscape, program and national scale. This research directly addresses this need and the ‘Programmatic scale’ question of Task 1 of the JFSP Project Announcement FA-FON 14-5. The objectives of this study were three-fold. The first objective was to generate cost effective fuel treatment programs at the landscape scale and their impact on the preparedness program. The second objective was to quantify the interrelationship between the fuel program and preparedness program by budget alternative at a landscape scale to provide mangers with the fuel and preparedness budgets that achieve the highest return on investment (ROI) for any combination of budgets. The third objective was to form cost effective national and regional fuel treatment programs based on the data collected from the landscape analysis that considers national and regional policies. Using four ecologically distinct study sites across the United States the research demonstrates how a landscape level analysis is applied to a national analysis to generate a national budget that promotes the highest return on investment meeting the objectives of this research. The landscape analysis consists of identifying fuel treatments that promote the highest ROI while taking into account policy guidance for managing vegetation and fuels. The treatment locations are identified at each study site for a set of budget alternatives. The value added for each budget alternative was estimated by comparing the pre-treatment and post-treatment landscapes. The post-treatment landscape for each budget alternative was used to determine the effects on preparedness at increasing preparedness budget alternatives. The data was recorded as discrete combinations of fuels and preparedness budgeting. These discrete data points were applied to a programmatic analysis. A smooth three-dimensional surface was created using translog production functions (production functions) unique to each study site. A gradient method was applied to the production functions to increase the budget along the direction of the highest investment returns. A national analysis was generated by combining the preparedness and fuels budget reallocations determined from the program analysis for each of the individual study sites to create national budget allocations for each study site for each of the fuels and preparedness programs that maintain the landscape consideration for policy guidelines. The resulting output can guide managers to cost-effective budget allocation for wildland fire programs that yield the greatest value added without disrupting the workforce and the capital equipment allocations. The research demonstrates several key findings that include: the use of a common performance metric of ROI that can be applied from the landscape, to a program and to a national scale while reflecting DOI policy; the generation of programmatic valued production functions for fuels and preparedness as well as funding gradients to guide budget decisions along the surface; and lastly an extension of these methods that demonstrate how a national budgeting application can be generated.

Citation: Rideout DB, Wei Y, Kirsch AG, Brooks B, Kernohan N, Magbual B. 2018. Programmatic Analysis of Fuel Treatments: from the landscape to the national level. Joint Fire Science Project 14-5-01-1. Fort Collins, CO: Colorado State University. 38 p.
Topic(s): Fire & Economics, Fuel Treatments & Effects, Fuels
Ecosystem(s): None
Document Type: Technical Report or White Paper
NRFSN number: 16989
FRAMES RCS number: 25607
Record updated: May 24, 2018