Project Title: Wildfire risk framework for strategic planning
Principal Investigator: Alan A. Ager, Pacific Northwest Research Station, Western Wildland Environmental Threat Assessment Center, Prineville, OR 97754
Collaborator: Mark Finney, Rocky Mountain Research Station; Sue Stewart, Washington Office Fire and Aviation Management
E-mail Contact: aager[at]fs.fed.us
Summary: Empirical data on fire size distribution in the Western USA support the argument that large fire spread is the major determinant of wildfire risk. For instance, on the Deschutes National Forest in central Oregon, USA, the historical record for mapped fires (> 1.18 ha) between 1908 to 2003 shows that a mere 10 percent of the fires accounted for 74 percent of the total burned area (156,648 ha). These data indicate that the probability of a fire at a specific location is primarily determined by spread of large fires rather than local fuel conditions. This consideration has not been incorporated into the extensive array of wildfire risk models that are employed by public agencies to assess and respond to wildfire risk. Furthermore, because risk is the probability of actual loss, a wildfire risk model must also consider fire intensity and fire effects to be a useful tool for assessing the potential impact of fire on resources of concern.
The objectives of this project are to incorporate large-fire spread and intensity into a quantitative risk framework, and apply the framework on fire-prone federally managed lands in central Oregon to test several hypotheses regarding fire spread and effects on federal land management strategies. Specifically, we hypothesize that wildfire risk to highly valued resources within forest reserves (e.g., riparian buffers, wildlife corridors, visual retention areas, research natural areas) is primarily a function of large-fire behavior in the surrounding meso-landscape. Thus the ongoing debate about the benefits of managing reserves and impacts of fuel treatment on species of concern does not consider the proper scale of wildfire risk. This project also will test the application of probabilistic risk analysis to quantify wildfire threats to various resources of concern, including desired future forest conditions as described by seral stages and structure. We will develop and apply loss functions for specific forest resource values and couple these functions with burn probability outputs to quantify probabilistic loss under a range of wildfire scenarios. Finally, we will test a range of spatially explicit fuel treatment scenarios to understand how spatial patterns of management activities affect wildfire losses to specific forest reserve systems on national forest lands.
The wildfire risk framework is being tested in a 1-million-ha area surrounding the Ochoco National Forest.
Burn probabilities will be estimated by simulation using the minimum travel time fire growth algorithms of Finney (2002) as implemented in a modified version of FlamMap (Finney 2006; Finney et. al. 2007, Ager et al. 2007). The wildfire burn conditions will be varied to simulate fire weather scenarios that range from 70 to 120 percent of historical (10 to 20 years) weather as determined from analysis of remote weather station data (http://www.raws.dri.edu). Wildfire burn conditions include parameters for windspeed, wind direction, fuel moisture, and burn period. Weather data from recent extreme fire events on the two respective national forests will also be used to calibrate and refine weather scenarios. It is assumed that wildfire behavior under these extreme conditions is largely independent of suppression effort, an assumption that is well supported in the literature (e.g., Finney 2005). Ignition location will be assumed to be random, and sufficient ignitions will be simulated to obtain robust estimates of a burn probability for each pixel on the landscape. Simulations will be performed at 90- x 90-m pixel resolution using a 64 bit, 16 duo-core processor computer housed at a Forest Service computer facility.
The wildfire simulations output the burn probability for each pixel and a frequency distribution of flame lengths observed for each pixel in 0.5-m classes over all simulated fires. The burn probability for a given pixel is an estimate of the likelihood that a pixel will burn given a random ignition within the study area under the defined burn conditions. The conditional probability of resource loss or impact is further defined as the proportion of simulated fires in each pixel that exceeded the lethal flame length for a given stand and resource value. To determine the lethal flame length, the tree list inventory representing each pixel will be burned within FVS-FFE with flame lengths ranging from 0.5 to 15 m in 0.5-m increments (SIMFIRE and FLAMEADJ keywords in FVS-FFE). FVS-FFE uses several fire behavior models as described in Andrews (1976), van Waggoner (1977), Scott and Reinhardt (2001) to predict fire spread, intensity, and crown fire initiation. Tree mortality following fire is predicted according to the methods implemented in FOFEM (Reinhardt et al., 1997). The postwildfire stand tree list will be then examined to determine the threshold flame length at which specific resource criteria are lost (e.g., large trees, canopy closure, down wood, snags, and other forest plan standards). Wildfire risk to specific reserves will be calculated as
Expected (net value change) = sum(p(Fi)(Bij-Lij)
where p(Fi) is defined as the probability of the ith fire behavior at a specific location over N fires and Bij and Lij are the benefits and losses afforded for the jth value of M values received from the ith fire behavior. The expected net value change can include financial, ecological, or other values at present day or future discounted values. In the present study wildfire benefits will not be considered.
Fuel Treatment Simulation
Fuel treatment will be simulated on individual stands using the Southern Oregon variant of the Forest Vegetation Simulator (FVS, Dixon, 2003). FVS is an individual-tree, distance-independent growth and yield model that is extensively used to model fuel treatments and other stand management activities. Spatial fuel treatment constraints and priorities will be modeled within the FVS Parallel Processing Extension (FVS-PPE, Crookston and Stage, 1991). Treatment scenarios will call for a range of treatment intensities (5 to 30 percent of forested federal lands) and treatments will be strategically located to slow fire spread into specific forest reserve types as demonstrated in Ager et al. (2007). Fuel treatment prescriptions will mimic operational practices on the forest.
• Maps depicting the spatial patterns of risk for specific forest reserves
• Evaluation of hypotheses concerning the effects of past management strategies on wildfire risk–i.e., did lack of active management within reserves affect wildfire risk?
• Partitioning of risk factors to measure the relative contribution of fire spread, intensity, and loss function to overall risk
• Effectiveness of various wildfire mitigation strategies
Management areas for the Ochoco National Forest
Example burn probabity outputs for the study area (below).
High-probability areas are depicted in red.
Ager, A.; Finney, M., Kerns, B.; Maffei, H. 2007. Modeling Wildfire Risk to Late Successional Forest Reserves in the Pacific Northwest, USA. Forest Ecology and Management 246:45-56
Ager, A.; McMahan, A.; Barrett, J.; McHugh, C. 2006. A simulation study of forest restoration and fuels treatments on a wildland-urban interface. Landscape and Urban Planning 80:292-300.
Andrews, P.L. 1986. BEHAVE: fire behavior prediction and fuel modeling system – BURN subsystem, Part 1. USDA Forest Service, General Technical Report INT-194.
Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behaviour. USDA Forest Service Intermountain Forest and Range Experiment Station, General Technical Report INT-GTR-122.
Crookston, N.L.; Stage, A.R. 1991. User’s guide to the Parallel Processing Extension of the Prognosis Model. USDA Forest Service, Rocky Mountain Research Station General Technical Report INT-281.
Dixon, G.E. 2003. Essential FVS: A user’s guide to the Forest Vegetation Simulator. Internal Report USDA Forest Service, Forest Management Service Center. Fort Collins, CO.
Finney, M.A. 2001. Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. For. Sci. 47(2), 219-228.
Finney, M.A. 2002. Fire growth using minimum travel time methods. Can. J. For. Res. 32, 1420-1424.
Finney, M.A. 2005. The challenge of quantitative risk analysis for wildland fire. For. Ecol. Manage. 211, 97-108.
Finney, M.A. 2006. An overview of FlamMap fire modeling capabilities. In: Andrews, P.L., Butler, B.W. (Comps), Fuels Management-How to Measure Success: Conference Proceedings. 28-30 March 2006; Portland, OR. USDA Forest Service, Rocky Mountain Research Station Proceedings RMRS-P-41. 809 p. Fort Collins, CO. p213-220
Finney, M.A.; Seli, R.C.; McHugh, C.; Ager, A.; Bahro, B.; Agee, J.K. 2007. Simulation of long-term landscape-level fuel treatment effects on large wildfires. International Journal of Wildland Fire 16:712–727
Reinhardt, E.D.; Keane, R.E., Brown, J.K. 1997. First order fire effects model: FOFEM. USDA Forest Service General Technical Report INT-GTR-344. 65 p.
Reinhardt, E.; Crookston, N.L., tech. eds. 2003. The Fire and Fuels Extension to the Forest Vegetation Simulator. USDA Forest Service, Rocky Mountain Research Station Gen. Tech. Rep. RMRS-GTR-116. Ogden, UT. 209 p.
Scott, J.H.; Burgan, R.E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO. 72 p.
Scott, J., Reinhardt, E.D. 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service, Rocky Mountain Research Station Research Paper RMRS-RP-29. 59 p.
Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Can. J. for. Res. 7, 23-34.
See related WWETAC project: Risk science plan for the Joint Fire Science Program
Project ID: FY07AA30