A Geospatial Framework to Assess Fireline Effectiveness for Large Wildfires in the Western USA

Quantifying fireline effectiveness (FLE) is essential to evaluate the efficiency of large wildfire management strategies to foster institutional learning and improvement in fire management organizations. FLE performance metrics for incident-level evaluation have been developed and applied to a small set of wildfires, but there is a need to understand how widely they vary across incidents to progress towards targets or standards for performance evaluation. Recent efforts to archive spatially explicit fireline records from large wildfires facilitate the application of these metrics to a broad sample of wildfires in different environments. We evaluated fireline outcomes (burned over, held, not engaged) and analyzed incident-scale FLE for 33 large wildfires in the western USA from the 2017 and 2018 fire seasons. FLE performance metrics varied widely across wildfires and often aligned with factors that influence suppression strategy. We propose a performance evaluation framework based on both the held to engaged fireline ratio and the total fireline to perimeter ratio. These two metrics capture whether fireline was placed in locations with high probability of engaging with the wildfire and holding and the relative level of investment in containment compared to wildfire growth. We also identify future research directions to improve understanding of decision quality in a risk-based framework.

[1]  Andrew A. G. Wilson.  Width of firebreak that is necessary to stop grass fires: some field experiments , 1988 .

[2]  Matthew P. Thompson,et al.  Examining heterogeneity and wildfire management expenditures using spatially and temporally descriptive data , 2016 .

[3]  J. Bayham,et al.  Resource Allocation under Fire , 2020, Land Economics.

[4]  S. Lieske,et al.  The Impact of Residential Development Pattern on Wildland Fire Suppression Expenditures , 2016, Land Economics.

[5]  Crystal S. Stonesifer,et al.  Wildfire Response Performance Measurement: Current and Future Directions , 2018, Fire.

[6]  Matthew P. Thompson,et al.  Quantifying the influence of previously burned areas on suppression effectiveness and avoided exposure: A case study of the Las Conchas Fire , 2016 .

[7]  Matthew P. Thompson,et al.  Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making , 2011 .

[8]  Matthew P. Thompson,et al.  Studying interregional wildland fire engine assignments for large fire suppression , 2017 .

[9]  Matthew P. Thompson,et al.  The Exposure Index: Developing firefighter safety performance measures , 2011 .

[10]  Matthew P. Thompson,et al.  A review of challenges to determining and demonstrating efficiency of large fire management , 2017 .

[11]  Matthew P. Thompson,et al.  Wildfire risk science facilitates adaptation of fire-prone social-ecological systems to the new fire reality , 2020, Environmental Research Letters.

[12]  Matthew P. Thompson,et al.  Negative consequences of positive feedbacks in US wildfire management , 2015, Forest Ecosystems.

[13]  Jeremy S. Fried,et al.  Simulating wildfire containment with realistic tactics , 1996 .

[14]  H. Anderson.  Aids to Determining Fuel Models for Estimating Fire Behavior , 1982 .

[15]  Thomas J. Duff,et al.  Improving Fire Behaviour Data Obtained from Wildfires , 2018 .

[16]  Matthew P. Thompson,et al.  Rethinking the Wildland Fire Management System , 2018, Journal of Forestry.

[17]  P. Fulé,et al.  Previous fires and roads limit wildfire growth in Arizona and New Mexico, U.S.A. , 2019, Forest Ecology and Management.

[18]  Sarah McCaffrey,et al.  Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts , 2018 .

[19]  David E. Calkin,et al.  Engaging the fire before it starts: A case study from the 2017 Pinal Fire (Arizona) , 2019 .

[20]  R. Bradstock,et al.  The effect of fuel age on the spread of fire in sclerophyll forest in the Sydney region of Australia , 2010 .

[21]  Branda Nowell,et al.  Redundancy as a Strategy in Disaster Response Systems: A Pathway to Resilience or a Recipe for Disaster? , 2017 .

[22]  Yu Wei,et al.  Risk Management and Analytics in Wildfire Response , 2019, Current Forestry Reports.

[23]  K. Reynolds,et al.  Strategic and tactical planning to improve suppression efforts against large forest fires in the Catalonia region of Spain , 2019, Forest Ecology and Management.

[24]  Matthew P. Thompson,et al.  Weather, Risk, and Resource Orders on Large Wildland Fires in the Western US , 2020 .

[25]  Matthew P. Thompson,et al.  Getting Ahead of the Wildfire Problem: Quantifying and Mapping Management Challenges and Opportunities , 2016 .

[26]  D. J. Strauss,et al.  Allocating resources to large wildland fires: a model with stochastic production rates , 1992 .

[27]  Matt P. Plucinski,et al.  Fighting Flames and Forging Firelines: Wildfire Suppression Effectiveness at the Fire Edge , 2019, Current Forestry Reports.

[28]  Timothy Ingalsbee.  Ecological fire use for ecological fire management: Managing large wildfires by design , 2015 .

[29]  Charles W. McHugh,et al.  Modeling containment of large wildfires using generalized linear mixed-model analysis , 2009 .

[30]  Patrick H. Freeborn,et al.  Towards improving wildland firefighter situational awareness through daily fire behaviour risk assessments in the US Northern Rockies and Northern Great Basin , 2017 .

[31]  David E Calkin,et al.  Production and efficiency of large wildland fire suppression effort: A stochastic frontier analysis. , 2016, Journal of environmental management.

[32]  Nathan P. Mietkiewicz,et al.  Switching on the Big Burn of 2017 , 2018, Fire.

[33]  Fermín J. Alcasena,et al.  Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region , 2020, Risk analysis : an official publication of the Society for Risk Analysis.

[34]  Alexandra D. Syphard,et al.  Comparing the role of fuel breaks across southern California national forests , 2011 .

[35]  Matthew P. Thompson,et al.  An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management , 2017 .

[36]  Matthew P. Thompson,et al.  A framework for developing safe and effective large-fire response in a new fire management paradigm , 2017 .

[37]  Matthew P. Thompson,et al.  Modelling suppression difficulty: current and future applications , 2020 .

[38]  J. N. Westwood,et al.  PACIFIC SOUTHWEST Forest and Range Experiment Station , 1970 .

[39]  David E. Calkin,et al.  Econometric analysis of fire suppression production functions for large wildland fires , 2013 .

[40]  Matt P. Plucinski,et al.  Contain and Control: Wildfire Suppression Effectiveness at Incidents and Across Landscapes , 2019, Current Forestry Reports.

[41]  Jeremy S. Fried,et al.  Expert opinion estimation of fireline production rates , 1989 .

[42]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[43]  Matthew P. Thompson,et al.  The Role of Previous Fires in the Management and Expenditures of Subsequent Large Wildfires , 2019 .