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Performance measurement and suppression effectiveness

Status: 
Action
Dates: 
January, 2015

Wildfire

The USDA Forest Service manages thousands of wildfires across the United States each year. More frequent occurrence of large, costly wildfires and lengthening fire seasons have contributed, among other factors, to rising expenditures. This increased spending is a mounting concern for Congress, non-Federal partners, Federal Agencies, and the public, as the USDA Forest Service struggles to sustain its non-fire programs with limited funds. The fire management system can effectively deploy and manage many thousands of firefighting resources (firefighters, crews, engines, aircraft, etc.) to respond to a large wildfire, yet it is difficult within that same system to recreate what happened in a large fire response to:  1) demonstrate what the investment looked like, 2) assess what worked/didn’t work, and 3) plan to improve efficiency in the future based on what was learned.  The Wildfire Risk Management Team is addressing this knowledge gap related to resource effectiveness and performance measurement through various efforts.

First, the Team is examining resource effectiveness through a series of empirical studies from recent wildfires that track daily resource use, including aviation and ground-based fire suppression resources, to assess the effects of resource use on wildfire containment under a range of environmental conditions. Initial efforts related to this work have been published, and expansion of the analysis scope is underway.

The Team continues to summarize and analyze yearly, national-scale large airtanker use to provide the only comprehensive source of spatially-explicit Agency airtanker activity. This drop information has been compiled for 8 years of record (2010-2017), and current efforts will update previous publications, illustrate temporal trends, and link the drop activity back to a gridded fire weather potential product.

Efforts to improve the way resources move in response to fire demand are also ongoing. The Team has modeled regional, and intraregional movement of specific resource types to demonstrate inefficiencies within the system. The Team is working directly with the Interagency Hotshot community to develop a resource allocation model that minimizes travel while controlling for crew fatigue and other factors.

Additionally, the Team is developing and applying empirically driven models of firefighting resource effectiveness considering resource type, mission objective, and incident characteristics to improve the efficiency of wildfire management. Specific tasks are designed to improve the ability to:

  • Define, measure, and illustrate attributes of risk-informed, efficient incident management
  • Identify conditions under which wildfire management strategies, tactics, and actions are likely to be cost-effective
  • Identify factors driving incident decision making, including risk attitudes, incentives, institutional arrangements, sociopolitical expectations, and incident characteristics
  • Identify factors driving suppression costs, and to distinguish between those that are and are not within the scope of fire managers’ control
  • Identify more direct linkages between incident decision making, firefighting resource use, and suppression costs
  • Develop and refine empirically driven models of suppression costs to benchmark observed costs and to analyze likely future costs across geographic scales and areas

Finally, the Team is working directly with the Washington Office to develop and implement a set of Key Performance Indicators (KPIs) to provide a comprehensive approach to performance measurement and accountability in the context of fire management. The use of KPIs provides a disciplined approach for managing performance and tracking progress over time. Initial KPIs in development include indices that will measure ground exposure, resource use, fire line effectiveness, and air tanker drop conditions. Historical baseline measurements are nearly complete, and efforts to track some KPIs during the active fire season should occur in 2018.

Publications

Belval, Erin J. ; Wei, Yu ; Calkin, Dave E. ; Stonesifer, Crystal S. ; Thompson, Matthew P. ; Tipton, John R. , 2017
Hand, Michael ; Katuwal, Hari ; Calkin, Dave E. ; Thompson, Matthew P. , 2017
Wei, Yu ; Belval, Erin J. ; Thompson, Matthew P. ; Calkin, Dave E. ; Stonesifer, Crystal S. , 2016
Stonesifer, Crystal S. ; Thompson, Matthew P. ; Calkin, Dave E. ; McHugh, Charles W. , 2015
Stonesifer, Crystal S. ; Calkin, Dave E. ; Thompson, Matthew P. ; Kaiden, Jeffrey D. , 2014
Calkin, Dave E. ; Stonesifer, Crystal S. ; Thompson, Matthew P. ; McHugh, Charles W. , 2014
Thompson, Matthew P. ; Calkin, David E. ; Herynk, Jason ; McHugh, Charles W. ; Short, Karen C. , 2012
Calkin, Dave E. ; Phipps, John ; Holmes, Tom ; Rieck, Jon ; Thompson, Matthew P. , 2011

Deliverables

A survey of federal fire managers to characterize perceptions related to resource importance, scarcity, and substitutability by resource type.

Each year, federal fire managers draw from a shared pool of suppression resources to meet highly variable fire suppression demands. Historically, Resource Ordering and Status System (ROSS) dispatch reports have been used to track supply and demand, including unmet demand of suppression resources ordered for fire management. We surveyed federal fire managers, operations personnel, and line officers responsible for ordering suppression resources to characterize ordering patterns and perceptions related to resource importance, scarcity, and substitutability. With this survey, we explore how the results affect tradeoff analyses, operational efficiency, and risk management practices in federal fire management.



National Strategic Program Areas: 
Wildland Fire and Fuels
RMRS Science Program Areas: 
Human Dimensions
RMRS Strategic Priorities: 
Fire Sciences
Geography: 
National
Project Contact: 

Principal Investigators:
Co-Investigators:
Collaborators:
Hari Katuwal - University of Montana
Yu Wei - Colorado State University
Erin Belval - Colorado State University

Research Staff: