Towards enhanced risk management: planning, decision making and monitoring of US wildfire response

Dunn, Christopher J.; Calkin, David E.; Thompson, Matthew P.

doi:10.1071/wf17089

Cited by 37 publications

(24 citation statements)

References 34 publications

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“…Finally, we were unable to discern the effects of fire suppression activities and whether they varied by ownership, since incident documentation of suppression activities are generally not collected or maintained in a manner consistent with quantitative or geospatial statistical analyses (Dunn et al. ).…”

Section: Limitationsmentioning

confidence: 99%

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Zald

Dunn

2018

Ecological Applications

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136

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Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 [mean ± SE]) vs. BLM forests (398.87 ± 18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

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Section: Limitationsmentioning

confidence: 99%

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Zald

Dunn

2018

Ecological Applications

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136

120

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“…Equation (5) uses a pair of equations to capture the logic that containment effort is needed at the boundary between POD i and j to stop fire spreading between them only if fire is already in one of them. Equation (6) ensures that fire could spread out from POD i only after it burns i. Equation (7) ensures that if fire reaches POD i, it must have spread into it from one of its adjacent PODs j unless i is one of the fire-starting PODs.…”

Section: Mathematical Formulation For Rpod Selection (The First Step mentioning

confidence: 99%

“…Wildfire management can be a complex decision-making process involving uncertain and dynamic conditions, and requiring rapid assimilation of multiple types of information from various sources [1,2]. Operations research (OR) models can be used to help integrate fire data, suggest management strategies, and conduct tradeoff analysis to assist fire decision processes [3][4][5][6]. OR models developed for wildfire management can take many forms, ranging from pre-fire applications determining optimal investment portfolios in prevention/suppression activities along with optimal stationing and deployment rules for suppression resources [7][8][9][10][11][12], to post-fire applications determining optimal mitigation strategies for reducing debris flow risk [13].…”

Section: Introductionmentioning

confidence: 99%

Designing Operationally Relevant Daily Large Fire Containment Strategies Using Risk Assessment Results

Wei

Thompson

Scott³

et al. 2019

Forests

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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.

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“…The lack of a comprehensive approach to performance measurement and accountability in the context of fire management is a well-identified issue [12]. A primary recommendation of the 2014 Quadrennial Fire Review was to "develop key performance indicators for all core programs and begin targeted data collection to support evaluation of the effectiveness of those programs" [13].…”

Section: Background On Performance Measurement In the Forest Servicementioning

confidence: 99%

Wildfire Response Performance Measurement: Current and Future Directions

Thompson

Lauer

Calkin

et al. 2018

Fire

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The Forest Service, U.S. Department of Agriculture, defines success in the wildland fire response environment as "safely achieving reasonable objectives with the least firefighter exposure necessary while enhancing stakeholder support for our management efforts". However, persistent information and knowledge gaps challenge the agency's ability to measure success in coming fire seasons. In this paper, we outline a roadmap to help fill these gaps, describing progress towards developing meaningful fire response key performance indicators (KPIs). We focus on characterizing suppression resource use and effectiveness as requisite initial steps towards reducing unnecessary exposure. Our intentions are to articulate the rationale for embracing KPIs for fire response operations, briefly review best practices as they relate to organizational performance measurement, and describe recent and emerging analysis techniques designed to ultimately improve responder exposure assessment. Specifically, we review tangible research products that could be operationalized as KPIs in the near future, and illustrate their calculation and interpretation for a set of large fires that occurred in the U.S. in 2017. To conclude, we offer thoughts on productive pathways forward with performance measurement.

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Towards enhanced risk management: planning, decision making and monitoring of US wildfire response

Cited by 37 publications

References 34 publications

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Designing Operationally Relevant Daily Large Fire Containment Strategies Using Risk Assessment Results

Wildfire Response Performance Measurement: Current and Future Directions

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