Dave Calkin

Uncertainty and risk in wildland fire management: a review.

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making.

Integrated wildfire risk assessment: Framework development and application on the Lewis and Clark National Forest in Montana, USA

The financial, socioeconomic, and ecological impacts of wildfire continue to challenge federal land management agencies in the United States. In recent years, policymakers and managers have increasingly turned to the field of risk analysis to better manage wildfires and to mitigate losses to highly valued resources and assets (HVRAs). Assessing wildfire risk entails the interaction of multiple components, including integrating wildfire simulation outputs with geospatial identification of HVRAs and the characterization of fire effects to HVRAs. We present an integrated and systematic risk assessment framework that entails 3 primary analytical components: 1) stochastic wildfire simulation and burn probability modeling to characterize wildfire hazard, 2) expert-based modeling to characterize fire effects, and 3) multicriteria decision analysis to characterize preference structures across at-risk HVRAs. We demonstrate application of this framework for a wildfire risk assessment performed on the Little Belts Assessment Area within the Lewis and Clark National Forest in Montana, United States. We devote particular attention to our approach to eliciting and encapsulating expert judgment, in which we: 1) adhered to a structured process for using expert judgment in ecological risk assessment, 2) used as our expert base local resource scientists and fire/fuels specialists who have a direct connection to the specific landscape and HVRAs in question, and 3) introduced multivariate response functions to characterize fire effects to HVRAs that consider biophysical variables beyond fire behavior. We anticipate that this work will further the state of wildfire risk science and will lead to additional application of risk assessment to inform land management planning.

The Science and Opportunity of Wildfire Risk Assessment

Wildfire management within the United States continues to increase in complexity, as the converging drivers of (1) increased development into fire-prone areas, (2) accumulated fuels from historic management practices, and (3) climate change potentially magnify threats to social and ecological values (Bruins et al., 2010; Gude et al., 2008; Littell et al., 2009). The need for wildfire risk assessment tools continues to grow, as land management agencies attempt to map wildfire risk and develop strategies for mitigation. Developing and employing wildfire risk assessment models can aid management decision-making, and can facilitate prioritization of investments in mitigating losses and restoring fire on fire prone landscapes. Further, assessment models can be used for monitoring trends in wildfire risk over space and across time.

Near-term probabilistic forecast of significant wildfire events for the Western United States

Fire danger and potential for large fires in the United States (US) is currently indicated via several forecasted qualitative indices. However, landscape-level quantitative forecasts of the probability of a large fire are currently lacking. In this study, we present a framework for forecasting large fire occurrence – an extreme value event – and evaluating measures of uncertainties that do not rely on distributional assumptions. The statistical model presented here incorporates qualitative fire danger indices along with other location and seasonal specific explanatory variables to produce maps of forecasted probability of an ignition becoming a large fire, as well as numbers of large fires with measures of uncertainties. As an example, 6 years of fire occurrence data from the Western US were used to study the utility of two fire danger indices: the 7-Day Significant Fire Potential Outlook issued by Predictive Services in the US and the National Fire Danger Rating’s Energy Release Component. This exercise highlights the potential utility of the quantitative risk index as a real-time decision support tool that can enhance managers’ abilities to discriminate among planning areas in terms of the likelihood and range of expected significant fire events. The approach is applicable wherever there are archived historical data from both observed fires and fire danger indices.

A simulation and optimisation procedure to model daily suppression resource transfers during a fire season in Colorado

Sharing fire engines and crews between fire suppression dispatch zones may help improve the utilisation of fire suppression resources. Using the Resource Ordering and Status System, the Predictive Services’ Fire Potential Outlooks and the Rocky Mountain Region Preparedness Levels from 2010 to 2013, we tested a simulation and optimisation procedure to transfer crews and engines between dispatch zones in Colorado (central United States) and into Colorado from out-of-state. We used this model to examine how resource transfers may be influenced by assignment shift length, resource demand prediction accuracy, resource drawdown restrictions and the compounding effects of resource shortages. Test results show that, in certain years, shortening the crew shift length from 14 days to 4 days doubles the yearly transport cost. Results also show that improving the accuracy in predicting daily resource demands decreases the engine and crew transport costs by up to 40%. Other test results show that relaxing resource drawdown restrictions could decrease resource transport costs and the reliance on out-of-state resources. The model-suggested assignments result in lower transport costs than did historical assignments.

Corrigendum to: Factors influencing large wildland fire suppression expenditures

There is an urgent and immediate need to address the excessive cost of large fires. Here, we studied large wildland fire suppression expenditures by the US Department of Agriculture Forest Service. Among 16 potential non-managerial factors, which represented fire size and shape, private properties, public land attributes, forest and fuel conditions, and geographic settings, we found only fire size and private land had a strong effect on suppression expenditures. When both were accounted for, all the other variables had no significant effect. A parsimonious model to predict suppression expenditures was suggested, in which fire size and private land explained 58% of variation in expenditures. Other things being equal, suppression expenditures monotonically increased with fire size. For the average fire size, expenditures first increased with the percentage of private land within burned area, but as the percentage exceeded 20%, expenditures slowly declined until they stabilised when private land reached 50% of burned area. The results suggested that efforts to contain federal suppression expenditures need to focus on the highly complex, politically sensitive topic of wildfires on private land.

Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA

Can fire potential forecasts assist with pre-positioning of fire suppression resources, which could result in a cost savings to the United States government? Here, we present a preliminary assessment of the 7-Day Fire Potential Outlook forecasts made by the Predictive Services program. We utilized historical fire occurrence data and archived forecasts to assess how well the 7-Day Outlook predicts wildfire ignitions and escaped fires, ultimately to help characterize the effectiveness of this tool for prepositioning national firefighting resources. The historical fire occurrence data track ignitions on all land ownerships; from this dataset, we established number and location of ignitions and final fire size for the years 2009-2011 for Predictive Service Areas (PSAs) within the Northwest and Southwest Geographic Areas. These data were then matched to the corresponding PSA and appropriate forecast for each of the seven days prior to the ignition date. Final fire size was used as a metric to establish whether an ignition escaped initial attack, with fires greater than 121.4 hectares (300 acres) considered escaped. Our results show that 7-Day Outlook values yield better-than-random prediction of large fire activity, although there is wide variation in this relationship among individual PSAs. In addition, the number of escaped fires increased with the number of ignitions, with this relationship showing a distinct regional pattern. Fires were more likely to escape during certain times of the year, with this season being earlier in the Southwest than in the Northwest. Significantly higher numbers of escaped fires per ignition occurred during days considered to be high risk by the meteorologist than on lower-risk days.