Special Issue "Forest Fire Suppression: Consequences, Management Approaches, and New Paradigms"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Dr. Matthew P. Thompson
Website
Guest Editor
USDA Forest Service, Rocky Mountain Research Station, 240 W Prospect, Fort Collins, CO 80526, USA
Interests: risk and decision analysis; operations research; forest engineering; wildland fire management
Special Issues and Collections in MDPI journals
Dr. David E Calkin
Website
Guest Editor
USDA ARS Rocky Mountain Research Station, US Forest Service, 800 East Beckwith Avenue, Missoula, Montana 59801-5801, USA
Interests: wildland fire risk assessment; identification of values-at-risk to wildland fire; decision support system development; performance measurement of wildland fire suppression; modeling and forecasting of wildfire suppression costs; social and managerial tradeoffs among resources affected by fire management
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The prospects for damaging forest fires are increasing in many areas around the globe, challenging society to develop innovative and more efficient solutions. In this Special Issue, we focus on one aspect of this complex socioecological problem: suppression response to unplanned forest fires. We conceive of suppression as a continuum, ranging from attempts at total fire exclusion to attempts at leveraging fire as a natural disturbance process. Keys to improving society’s relationship with fire include an improved understanding of the near-, mid-, and long-term consequences of current suppression approaches, an improved capacity to enhance the safety and effectiveness of suppression responses, and perhaps new ways of thinking about the role of fire suppression in the broader context of forest management. In particular, we are interested in papers that target new approaches and paradigms relative to status quo solutions. Relevant topics for papers in this Special Issue include, but are not limited to: decision support; uncertainty and risk; simulation and optimization; monitoring and performance measurement; effects of suppression on forest health and biodiversity; safety; and strategic and operational planning.

Dr. Matthew P. Thompson
Dr. David E Calkin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wildfire management
  • decision support
  • socioecological systems
  • forest health

Published Papers (5 papers)

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Research

Open AccessArticle
Fire Suppression Resource Scarcity: Current Metrics and Future Performance Indicators
Forests 2020, 11(2), 217; https://doi.org/10.3390/f11020217 - 14 Feb 2020
Abstract
Wildland fire occurrence is highly variable in time and space, and in the United States where total area burned can vary substantially, acquiring resources (firefighters, engines, aircraft, etc.) to respond to fire demand is an important consideration. To determine the composition and scale [...] Read more.
Wildland fire occurrence is highly variable in time and space, and in the United States where total area burned can vary substantially, acquiring resources (firefighters, engines, aircraft, etc.) to respond to fire demand is an important consideration. To determine the composition and scale of this set of suppression resources managers may utilize data produced by past supply and demand information. The key challenge with this approach is that there is currently no clear system of record to track suppression resource supply and demand, and there are potential pitfalls within existing systems that may provide misleading information regarding the true levels of resource scarcity. In this manuscript, we investigate the issue of resource scarcity by examining two key resources that operations personnel have identified as both high value and scarce: type 1 firefighting crews and large airtankers. We examine data from the Resource Ordering and Status System and analyze the level of resource scarcity indicated by these data over the 2014–2018 fire seasons. We focus on data metrics with potential utility for managers responsible for annual national-level decisions regarding crew and airtanker acquisition; some of these metrics are already used to inform such decisions. We examine the limitations of each metric and suggest new metrics that could more accurately reflect true resource use and scarcity. Such metrics could lead to a substantially improved decision-making process. Full article
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Open AccessArticle
Weather, Risk, and Resource Orders on Large Wildland Fires in the Western US
Forests 2020, 11(2), 169; https://doi.org/10.3390/f11020169 - 03 Feb 2020
Abstract
Research Highlights: Our results suggest that weather is a primary driver of resource orders over the course of extended attack efforts on large fires. Incident Management Teams (IMTs) synthesize information about weather, fuels, and order resources based on expected fire growth rather than [...] Read more.
Research Highlights: Our results suggest that weather is a primary driver of resource orders over the course of extended attack efforts on large fires. Incident Management Teams (IMTs) synthesize information about weather, fuels, and order resources based on expected fire growth rather than simply reacting to observed fire growth. Background and Objectives: Weather conditions are a well-known determinant of fire behavior and are likely to become more erratic under climate change. Yet, there is little empirical evidence demonstrating how IMTs respond to observed or expected weather conditions. An understanding of weather-driven resource ordering patterns may aid in resource prepositioning as well as forecasting suppression costs. Our primary objective is to understand how changing weather conditions influence resource ordering patterns. Our secondary objective is to test how an additional risk factor, evacuation, as well as a constructed risk metric combining fire growth and evacuation, influences resource ordering. Materials and Methods: We compile a novel dataset on over 1100 wildfires in the western US from 2007–2013, integrating data on resource requests, detailed weather conditions, fuel and landscape characteristics, values at risk, fire behavior, and IMT expectations about future fire behavior and values at risk. We develop a two-step regression framework to investigate the extent to which IMTs respond to realized or expected weather-driven fire behavior and risks. Results: We find that IMTs’ expectations about future fire growth are influenced by observed weather and that these expectations influence resource ordering patterns. IMTs order nearly twice as many resources when weather conditions are expected to drive growth events in the near future. However, we find little evidence that our other risk metrics influence resource ordering behavior (all else being equal). Conclusion: Our analysis shows that incident management teams are generally forward-looking and respond to expected rather than recently observed weather-driven fire behavior. These results may have important implications for forecasting resource needs and costs in a changing climate. Full article
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Open AccessArticle
Modeling Ground Firefighting Resource Activities to Manage Risk Given Uncertain Weather
Forests 2019, 10(12), 1077; https://doi.org/10.3390/f10121077 - 27 Nov 2019
Abstract
Wildland firefighting requires managers to make decisions in complex decision environments that hold many uncertainties; these decisions need to be adapted dynamically over time as fire behavior evolves. Models used in firefighting decisions should also have the capability to adapt to changing conditions. [...] Read more.
Wildland firefighting requires managers to make decisions in complex decision environments that hold many uncertainties; these decisions need to be adapted dynamically over time as fire behavior evolves. Models used in firefighting decisions should also have the capability to adapt to changing conditions. In this paper, detailed line construction constraints are presented for use with a stochastic mixed integer fire growth and behavior program. These constraints allow suppression actions to interact dynamically with stochastic predicted fire behavior and account for many of the detailed line construction considerations. Such considerations include spatial restrictions for fire crew travel and operations. Crew safety is also addressed; crews must keep a variable safety buffer between themselves and the fire. Fireline quality issues are accounted for by comparing control line capacity with fireline intensity to determine when a fireline will hold. The model assumes crews may work at varying production rates throughout their shifts, providing flexibility to fit work assignments with the predicted fire behavior. Nonanticipativity is enforced to ensure solutions are feasible for all modeled weather scenarios. Test cases demonstrate the model’s utility and capability on a raster landscape. Full article
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Open AccessArticle
A Temporal Framework of Large Wildfire Suppression in Practice, a Qualitative Descriptive Study
Forests 2019, 10(10), 884; https://doi.org/10.3390/f10100884 - 07 Oct 2019
Cited by 2
Abstract
Suppression activities on large wildfires are complicated. Existing suppression literature does not take into account this complexity which leaves existing suppression models and measures of resource productivity incomplete. A qualitative descriptive analysis was performed on the suppression activities described in operational documents of [...] Read more.
Suppression activities on large wildfires are complicated. Existing suppression literature does not take into account this complexity which leaves existing suppression models and measures of resource productivity incomplete. A qualitative descriptive analysis was performed on the suppression activities described in operational documents of 10 large wildfires in Victoria, Australia. A five-stage classification system summarises suppression in the everyday terms of wildfire management. Suppression can be heterogeneous across different sectors with different stages occurring across sectors on the same day. The stages and the underlying 20 suppression tasks identified provide a fundamental description of how suppression resources are being used on large wildfires. We estimate that at least 57% of resource use on our sample of 10 large wildfires falls outside of current suppression modelling and productivity research. Full article
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Open AccessArticle
Designing Operationally Relevant Daily Large Fire Containment Strategies Using Risk Assessment Results
Forests 2019, 10(4), 311; https://doi.org/10.3390/f10040311 - 05 Apr 2019
Cited by 7
Abstract
In this study, we aim to advance the optimization of daily large fire containment strategies for ground-based suppression resources by leveraging fire risk assessment results commonly used by fire managers in the western USA. We begin from an existing decision framework that spatially [...] Read more.
In this study, we aim to advance the optimization of daily large fire containment strategies for ground-based suppression resources by leveraging fire risk assessment results commonly used by fire managers in the western USA. We begin from an existing decision framework that spatially overlays fire risk assessment results with pre-identified potential wildland fire operational delineations (PODs), and then clusters PODs into a response POD (rPOD) using a mixed integer program (MIP) model to minimize expected loss. We improve and expand upon this decision framework through enhanced fire modeling integration and refined analysis of probabilistic and time-sensitive information. Specifically, we expand the set of data inputs to include raster layers of simulated burn probability, flame length probability, fire arrival time, and expected net value change, all calculated using a common set of stochastic weather forecasts and landscape data. Furthermore, we develop a secondary optimization model that, for a given optimal rPOD, dictates the timing of fire line construction activities to ensure completion of containment line prior to fire arrival along specific rPOD edges. The set of management decisions considered includes assignment of PODs to be included in the rPOD, assignment of suppression resources to protect susceptible structures within the rPOD, and assignment of suppression resources to construct fire lines, on specific days, along the perimeter of the rPOD. We explore how fire manager risk preferences regarding firefighter safety affect optimal rPOD characteristics, and use a simple decision tree to display multiple solutions and support rapid assessment of alternatives. We base our test cases on the FSPro simulation of the 2017 Sliderock Fire that burned on the Lolo National Forest in Montana, USA. The overarching goal of this research is to generate operationally relevant decision support that can best balance the benefits and losses from wildfire and the cost from responding to wildfire. Full article
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