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An uncertainty analysis of wildfire modeling [Chapter 13]

Author(s): Karen L. Riley, Matthew P. Thompson
Editor(s): Karen L. Riley, Peter Webley, Matthew P. Thompson
Year Published: 2017

Before fire models can be understood, evaluated, and effectively applied to support decision making, model-based uncertainties must be analyzed. In this chapter, we identify and classify sources of uncertainty using an established analytical framework, and summarize results graphically in an uncertainty matrix. Our analysis facilitates characterization of the underlying nature of each source of uncertainty (inherent system variability versus limited knowledge), the location where it manifests within the modeling process (inputs, parameters, model structure, etc.), and its magnitude or level (on a continuum from complete determinism to total ignorance). We adapt this framework to the wildfire context by identifying different planning horizons facing fire managers (near‐, mid‐, and long‐term) as well as modeling domains that correspond to major factors influencing fire activity (fire behavior, ignitions, landscape, weather, and management). Our results offer a high‐level synthesis that ideally can provide a sound informational basis for evaluating current modeling efforts and that can guide more in‐depth analyses in the future. Key findings include: (1) uncertainties compound and magnify as the planning horizon lengthens; and (2) while many uncertainties are due to variability, gaps in basic fire-spread theory present a major source of knowledge uncertainty.

Citation: Riley, Karin; Thompson, Matthew. 2017. An uncertainty analysis of wildfire modeling [Chapter 13]. In: Riley, Karin; Webley, Peter; Thompson, Matthew, eds. Natural Hazard Uncertainty Assessment: Modeling and Decision Support, Geophysical Monograph 223, First Edition. American Geophysical Union. p. 193-213.
Topic(s): Fire Behavior, Fire Prediction, Simulation Modeling
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
Hot Topic(s): Fire Behavior Prediction
NRFSN number: 14997
Record updated: May 31, 2018