**4. Fire effects analysis & incorporation of expert judgment**

Estimating resource response to wildfire is a crucial step for quantitative risk assessment (Fairbrother & Turnley, 2005), and yet is also one of the most challenging steps. Effects analysis is made difficult by the scientific uncertainty and lack of data/information surrounding wildfire effects on non-market resources; specifically in that limited scientific understanding challenges characterization of marginal ecological changes, and further in that economic methods are immature for broad scale monetization of such changes (Keane & Karau 2010; Venn & Calkin, 2011). Expert systems are commonly used in natural resource management decision-making (González et al., 2007; Hirsch et al., 2004; Kaloudis et al., 2005; Vadrevu et al., 2010), and rely on the unique expertise and judgment of professionals as a proxy for empirical data. Increasingly in a variety of natural resource management applications researchers and practitioners are adopting structured approaches for eliciting and using expert knowledge (Kuhnert et al., 2010; Martin et al., 2009). Elicitation of expert judgment is particularly useful where decisions are time-sensitive and management or policy cannot wait for improved scientific knowledge (Knol et al., 2010).

Fig. 7. Analysis for the School Fire demonstrating the impact of implemented fuel

Estimating resource response to wildfire is a crucial step for quantitative risk assessment (Fairbrother & Turnley, 2005), and yet is also one of the most challenging steps. Effects analysis is made difficult by the scientific uncertainty and lack of data/information surrounding wildfire effects on non-market resources; specifically in that limited scientific understanding challenges characterization of marginal ecological changes, and further in that economic methods are immature for broad scale monetization of such changes (Keane & Karau 2010; Venn & Calkin, 2011). Expert systems are commonly used in natural resource management decision-making (González et al., 2007; Hirsch et al., 2004; Kaloudis et al., 2005; Vadrevu et al., 2010), and rely on the unique expertise and judgment of professionals as a proxy for empirical data. Increasingly in a variety of natural resource management applications researchers and practitioners are adopting structured approaches for eliciting and using expert knowledge (Kuhnert et al., 2010; Martin et al., 2009). Elicitation of expert judgment is particularly useful where decisions are time-sensitive and management or

**4. Fire effects analysis & incorporation of expert judgment** 

policy cannot wait for improved scientific knowledge (Knol et al., 2010).

treatments. Modified from (Cochrane et al., in press).

Structured Elicitation Process


Fig. 8. Eight major steps in organizing and implementing a structured elicitation of expert judgment. Modified from (Knol et al., 2010; Kuhnert et al., 2010)

Figure 8 presents a structured process for eliciting expert judgment. In the first step, a clear articulation of the research question(s) will inform the design and implementation of the study as well as the larger structure of the modeling process. This is followed by identification and characterization of uncertainties, which will influence choices regarding the type of experts and elicitation format. Resolving the scope and format of the elicitation entails identifying the number of experts to engage and the nature of the engagement (interview, group workshop, survey distribution, etc.), while considering available resources and other constraints. Selection of experts includes choices between generalists, subject matter experts, and normative experts (those with experience to support elicitation itself). Design of the protocol considers the type of information to be elicited, the most appropriate metric(s), the most appropriate elicitation mechanism, and how to clearly communicate information needs to avoid issues of linguistic uncertainty (Regan et al., 2002). Preparation of the elicitation protocol includes providing selected experts with sufficient information on the nature of the research problem and associated uncertainties, the scope and purpose of the elicitation, and the nature of the elicitation procedure itself. Lastly, the elicitation procedure is implemented, with opportunities for post-elicitation feedback and revision.

In terms of the wildfire management context, the research question generally involves assessing wildfire risk to HVRAs, potentially in a comparative risk framework to evaluate the effectiveness of alternative management actions (Figure 1). In the second step, wildfire management is subject to myriad sources of uncertainty, not all of which are necessarily best handled with expert judgment (burn probability modeling to capture environmental stochasticity, e.g.). Thompson & Calkin (2011) present a typology of uncertainties faced in wildfire management, and identify that with regard to knowledge uncertainty surrounding fire effects, expert systems are perhaps the most appropriate approach. In our past experience we have adopted group workshops, and assembled resource scientists (hydrologists, soil scientists, wildfire biologists, fire ecologists, etc.) as appropriate given the HVRAs being assessed (Thompson et al., 2011b). The elicitation protocol identifies response functions that quantitatively characterize resource-specific response functions as a function of fire intensity, and response functions are iteratively explored, justified, and updated until expert consensus is reached.

Figure 9 illustrates expert-based response functions for two HVRAs with contrasting response to fire, mapped across six fire intensity level (FIL) classes. These response functions

The Science and Opportunity of Wildfire Risk Assessment 113

In this section we briefly review a recently published example of integrated, national-scale wildfire risk assessment (Thompson et al., 2011b). The effort leveraged tools, datasets, and expertise of the Fire Program Analysis (FPA) system, a common interagency strategic decision support tool for wildland fire planning and budgeting (http://www.fpa.nifc.gov). We aggregated results according to eight geographic areas organized largely for purposes of incident management and mobilization of suppression resources: California (CA), Eastern Area (EA), Great Basin (GB), Northern Rockies (NR), Northwest (NW), Rocky Mountain

Fig. 10. Response functions plotting relative benefit/loss (y-axis) against fire intensity level

To map wildfire hazard we used wildfire simulation outputs (burn probability and intensity) from the FSim large fire simulator (Finney et al., 2011a), mapped at on a pixel basis (270m x 270m). In cooperation with the FPA Executive Oversight Group we identified seven key HVRA themes: residential structure locations, municipal watersheds, air quality, energy and critical infrastructure, federal recreation and recreation infrastructure, firesusceptible species, and fire-adapted ecosystems. Table 2 delineates the major HVRA themes along with identified sub-themes. We engaged ten fire and fuels program management officials from the Forest Service, National Park Service, Bureau of Land Management, Fish and Wildlife Service, and the Bureau of Indian Affairs to facilitate

(FIL; x-axis), for the Old Growth (OG) HVRA, sorted by dry/wet site.

response function assignments.

**5. Case study of wildfire risk** 

(RM), Southern Area (SA), and Southwest (SW).

were assigned in a group workshop format as part of a broader wildfire risk assessment conducted for the Lewis and Clark National Forest in Montana, United States. Aspen stands are highly valued because they provide habitat for a broad diversity of wildlife, and due to their relative rarity across the landscape. Aspens are reliant on wildfire for natural regeneration, and so are modeled as experiencing substantial benefit from fire at low to moderate intensities, with minor loss expected high intensity fires. For high investment infrastructure (e.g., developed campgrounds, cabins, ranger stations), damages are expected from any interaction with fire, and are expected to increase in severity as fire intensity increases.

Fig. 9. Response functions plotting relative NVC (y-axis) against fire intensity level (FIL; xaxis), for stands of aspen and high investment infrastructure.

Figure 10 displays additional response functions identified as part of the wildfire risk assessment for the Lewis and Clark National Forest. This figure highlights use of an additional geospatial variable, in this case moisture conditions on the site, to further refine fire effects estimates to old growth (OG) forest stands. Dry forests typically have evolved with and tend to receive a benefit from low to moderate intensity fires. At extreme intensities, high levels of mortality and damage are expected irrespective of site moisture conditions.
