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An Uncertainty Analysis of Wildfire Modeling

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An Uncertainty Analysis of Wildfire Modeling 13 An Uncertainty Analysis of Wildfire Modeling Karin Riley1 and Matthew Thompson2 ABSTRACT 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 igno- rance). 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 influenc- ing 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. 13.1. INTRODUCTION determined to a large degree by how well model‐based uncertainties are understood and communicated [Marcot 13.1.1. Wildfire Modeling and Uncertainty Analysis et al., 2012]. Analyzing model‐based uncertainties is a process of Land and fire managers rely on wildfire modeling tech- identifying, classifying, and evaluating sources of uncer- niques to better understand potential wildfire activity tainty and their influence on model outputs [Thompson and evaluate alternative risk management strategies. and Warmink, Chapter 2, this volume]. Identification and A wide range of wildfire models exists with varying classification, our principal foci here, are systematic and inputs, structures, outputs, and intended uses [Sullivan, iterative steps that articulate and characterize essential 2009a, 2009b, 2009c; Papadopolous and Pavlidou, 2011; attributes of uncertainties. These steps are a prerequisite Thompson and Calkin, 2011]. The ability of these models for subsequent evaluation of salient sources of uncer- to support efficient and effective risk management is tainty, which can range from qualitative expert review to computationally demanding quantitative techniques [Refsgaard et al., 2007; Jimenez et al., 2008; Duff et al., 1 Numerical Terradynamic Simulation Group, College of 2013]. Uncertainty analysis provides important informa- Forestry and Conservation, University of Montana, Missoula, Montana, USA tion for modelers and analysts, aiding selection of appro- 2 Rocky Mountain Research Station, US Forest Service, priate data and modeling techniques, and guiding model Missoula, Montana, USA calibration and validation efforts. It is crucial that Natural Hazard Uncertainty Assessment: Modeling and Decision Support, Geophysical Monograph 223, First Edition. Edited by Karin Riley, Peter Webley, and Matthew Thompson. © 2017 American Geophysical Union. Published 2017 by John Wiley & Sons, Inc. 193 194 NATURAL HAZARD UNCERTAINTY ASSESSMENT managers understand model‐based uncertainties as well, broader term referring to both wildfires and prescribed to establish confidence in results, and to determine the (i.e., intentionally ignited) fires; although much of this value of investing in additional data collection, research, chapter is likely applicable to the prescribed fire context, or more extensive modeling efforts. Adoption of a sys- our focus is on wildfires (i.e., unplanned ignitions)). The tematic, rigorous, and consistent process for analyzing management of a single wildfire incident evolves over the uncertainties facilitates the communication of important course of hours to weeks, with fire sizes ranging from less features of uncertainties faced within modeling and deci- than a hectare to over a million hectares. Fire growth is sion contexts. dictated by varying weather patterns, landscape condi- In this chapter, we focus on uncertainties related to the tions, and human responses. Across larger landscapes intersection of wildfire modeling and wildfire manage- and longer time horizons, uncertainty about the timing ment. Our primary objectives are (1) to illustrate applica- and location of ignitions, along with the weather condi- tion of uncertainty analysis to wildfire modeling, and (2) tions driving fire behavior, leads to reliance on risk‐based to introduce a conceptual framework that researchers characterization of wildfire variables to help support and managers can apply to guide future modeling and management decisions. At even larger spatiotemporal decision support efforts. Ideally, increased adoption of scales, long‐term strategic planning necessarily considers uncertainty analysis principles will lead to targeted and broader drivers of the human‐natural system, including efficient investments in gathering additional information, changes in wildfire policy, land use and vegetation dynam- improved communication between modelers and manag- ics, and climate change. These uncertainties pose great ers, and more informed decision‐making processes challenges to land and fire managers, who are increas- [Walker et al., 2003; Warmink et al., 2010]. ingly being asked to account for the effects of climate To begin, we briefly review conceptual models of wild- changes and human‐natural system dynamics in their fire activity to help set the stage for our uncertainty anal- land management plans (for example, US Forest Service ysis framework. Next, we introduce uncertainty analysis direction at http://www.fs.fed.us/emc/nepa/climate_change/ techniques in more detail, focusing on the identifica- includes/cc_land_mgmt_plan _rev_012010.pdf). tion and classification of sources of uncertainty, and To provide background, we review a simple conceptual describe how we tailored our analysis to the wildfire model of the major factors that influence the number, modeling context. We present results that identify where extent, and intensity of wildfires in the natural environ- uncertainties manifest in modeling processes, and how ment, in the absence of human management (Fig. 13.1). uncertainties vary as planning horizons change. Last, The frequency and location of ignitions (both human‐ we discuss implications of our findings and offer con- caused and lightning‐caused) determines the number cluding thoughts. of wildfires. Depending upon the location, ignition fre- quency can be driven predominately by human activity, 13.1.2. Conceptual Models of Wildfire Activity climatic and weather patterns that cause lightning, or some combination of the two. Recent weather conditions The dynamics of wildfire activity and management influence both fuel moisture (via recent precipitation, result from a coupling of human and natural systems relative humidity, and temperature) and rate of spread with complex feedback loops that operate across a (via wind and fuel moisture). Because weather influences broad spectrum of spatial and temporal scales [Liu et al., fuel moisture and rate of spread, it is a primary driver of 2007; Spies et al., 2014]. (Note that “wildland fire” is a fire intensity as well as fire extent. Landscape conditions Ignition Weather Landscape frequency & conditions conditions location Number, extent, and intensity of wildfires Figure 13.1 A simplified model of factors driving the number, extent, and intensity of wildland fires in the natural environment. (Note that, for example, weather is a source of ignitions, and that ignitions are affected by the land- scape as well, since an ignition must land on a receptive fuel in order to be viable. For the sake of simplicity, however, these relationships are not shown.) AN UNCERTAINTY ANALYsis of Wildfire ModeliNG 195 Ignition Weather Landscape frequency & conditions conditions location Number, extent, and intensity of wildfires Fire suppression actions Management actions Ignition prevention Fuel treatment programs Figure 13.2 A simplified model of factors driving the number, extent, and intensity of wildland fires and how management actions interact with wildfire in a coupled human‐natural system. relate to topography and the composition, structure, and actions outside of the wildfire context, such as climate continuity of fuels (flammable vegetation), which influ- change mitigation activities, are omitted from the figure, ence the intensity of wildfires in concert with weather. but could play a role. Next, we expand upon this model to illustrate the effects of management actions (Fig. 13.2). Wildfire inci- 13.2. METHODS dent response entails the strategic and tactical deploy- ment of firefighting resources that generally aim to 13.2.1. Identifying and Classifying Uncertainties restrict fire growth in order to minimize loss of highly valued resources while balancing safety and cost con- We consider three primary dimensions of uncertainty cerns. Wildfire management options extend beyond inci- in our analysis of wildfire modeling: (1) the underlying dent response to the midterm planning horizon, and cause or nature of the uncertainty; (2) where in the mod- include implementing prevention programs to reduce eling or decision process the uncertainty
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