Nicole M. Vaillant

Integrating fire behavior models and geospatial analysis for wildland fire risk assessment and fuel management planning

Wildland fire risk assessment and fuel management planning on federal lands in the US are complex problems that require state-of-the-art fire behavior modeling and intensive geospatial analyses. Fuel management is a particularly complicated process where the benefits and potential impacts of fuel treatments must be demonstrated in the context of land management goals and public expectations. A number of fire behavior metrics, including fire spread, intensity, likelihood, and ecological risk must be analyzed for multiple treatment alternatives. The effect of treatments on wildfire impacts must be considered at multiple scales. The process is complicated by the lack of data integration among fire behavior models, and weak linkages to geographic information systems, corporate data, and desktop office software. This paper describes our efforts to build a streamlined fuel management planning and risk assessment framework, and an integrated system of tools for designing and testing fuel treatment programs on fire-prone wildlands.

Restoration of fire in managed forests: a model to prioritize landscapes and analyze tradeoffs

Ongoing forest restoration on public lands in the western US is a concerted effort to counter the growing incidence of uncharacteristic wildfire in fire-adapted ecosystems. Restoration projects cover 725,000 ha annually, and include thinning and underburning to remove ladder and surface fuel, and seeding of fire-adapted native grasses and shrubs. The backlog of areas in need of restoration combined with limited budgets requires that projects are implemented according to a prioritization system. The current system uses a stand-scale metric that measures ecological departure from pre-settlement conditions. Although conceptually appealing, the approach does not consider important spatial factors that influence both the efficiency and feasibility of managing future fire in the post-treatment landscape. To address this gap, we developed a spatial model that can be used to explore different landscape treatment configurations and identify optimal project parameters that maximize restoration goals. We tested the model on a 245,000 ha forest and analyzed tradeoffs among treatment strategies as defined by fire behavior thresholds, total area treated, and the proportion of the project area treated. We assumed the primary goal as the protection and conservation of old growth ponderosa pine trees from potential wildfire loss. The model located optimal project areas for restoration and identified treatment areas within them, although the location was dependent on assumptions about acceptable fire intensity within restored landscapes, and the total treated area per project. When a high percentage of stands was treated (e.g., >80%), the resulting project area was relatively small, leaving the surrounding landscape at risk for fire. Conversely, treating only a few stands with extreme fire behavior (<20%) created larger projects, but substantial old growth forests remained susceptible to wildfire mortality within the project area. Intermediate treatment densities (35%) were optimal in terms of the overall reduction in the potential wildfire mortality of old growth. The current work expands the application in spatial optimization to the problem of dry forest restoration, and demonstrates a decision support protocol to prioritize landscapes and specific areas to treat within them. The concepts and model can be applied to similar problems in spatial ecology.

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.

ArcFuels10 system overview

Fire behavior modeling and geospatial analyses can provide tremendous insight for land managers as they grapple with the complex problems frequently encountered in wildfire risk assessments and fire and fuels management planning. Fuel management often is a particularly complicated process in which the benefits and potential impacts of fuel treatments need to be demonstrated in the context of land management goals and public expectations. The fuel treatment planning process is complicated by the lack of data assimilation among fire behavior models and weak linkages to geographic information systems (GIS), corporate data, and desktop office software. ArcFuels10 is a streamlined fuel management planning and wildfire risk assessment system that creates a trans-scale (stand to large landscape) interface to apply various forest growth and fire behavior models within an ArcGIS platform to design and test fuel treatment alternatives. The new version of ArcFuels has been implemented on Citrix at the Forest Service Enterprise Production Data Center, eliminating the need for desktop GIS, improving connectivity to the corporate geospatial databases housed at the data centers, and enabling sharing of information among Forest Service employees. This overview introduces ArcFuels10 and the tools available within the system. Further information, including download information, demonstration data, and a tutorial, can be found at http://www.fs.fed.us/wwetac/arcfuels/index.html.

Assessing Landscape Vulnerability to Wildfire in the USA

Wildfire is an ever present, natural process shaping landscapes. Having the ability to accurately measure and predict wildfire occurrence and impacts to ecosystem goods and services, both retrospectively and prospectively, is critical for adaptive management of landscapes. Landscape vulnerability is a concept widely utilized in the ecosystem management literature that has not been explicitly defined, particularly with regard to wildfire. Vulnerability more broadly is defined by three primary components: exposure to the stressor, sensitivity to a range of stressor variability, and resilience following exposure. In this synthesis, we define vulnerability in the context of wildfire. We first identify the components of a guiding framework for a vulnerability assessment with respect to wildfire. We then address retrospective assessments of wildfire vulnerability and the data that have been developed and utilized to complete these assessments. Finally, we review the modeling efforts that allow for predictive and probabilistic assessment of future vulnerability. Throughout the synthesis, we highlight gaps in the research, data availability, and models used to complete vulnerability assessments.

Fuel accumulation and forest structure change following hazardous fuel reduction treatments throughout California

Altered fuel conditions coupled with changing climate have disrupted fire regimes of forests historically characterisedbyhigh-frequencyandlow-to-moderate-severityfire.Managersusefueltreatmentstoabateundesirablefire behaviour and effects. Short-term effectiveness of fuel treatments to alter fire behaviour and effects is well documented; however, long-term effectiveness is not well known. We evaluated surface fuel load, vegetation cover and forest structure beforeandaftermechanicalandfire-onlytreatmentsover8yearsacross11NationalForestsinCalifornia.Eightyearspost treatment, total surface fuel load returned to 67 to 79% and 55 to 103% of pretreatment levels following fire-only and mechanical treatments respectively. Herbaceous or shrub cover exceeded pretreatment levels two-thirds of the time 8yearsaftertreatment.Fire-onlytreatmentswarrantedre-entryat8yearsposttreatmentowingtotheaccumulationoflive and dead fuels and minimal impact on canopy bulk density. In general, mechanical treatments were more effective at reducing canopy bulk density and initially increasing canopy base height than prescribed fire. However, elevated surface fuel loads, canopy base height reductions in later years and lack of restoration of fire as an ecological process suggest that including prescribed fire would be beneficial. Additional keywords: dry mixed conifer, mechanical treatments, moist mixed conifer, prescribed fire, yellow pine.

ArcFuels User Guide and Tutorial: for use with ArcGIS 9

Fuel management planning can be a complex problem that is assisted by fire behavior modeling and geospatial analyses. Fuel management often is a particularly complicated process in which the benefits and potential impacts of fuel treatments need to be demonstrated in the context of land management goals and public expectations. Fire intensity, likelihood, and effects can be analyzed for multiple treatment alternatives. Depending on the goal, the effect of treatments on wildfire impacts can be considered at multiple scales, from a single forest stand or planning unit to a watershed to a national forest to the Nation as a whole. The fuel treatment planning process is complicated by the lack of data assimilation among fire behavior models and by weak linkages to geographic information systems, corporate data, and desktop office software. ArcFuels is a streamlined fuel management planning and wildfire risk assessment system. ArcFuels creates a trans-scale (stand to large landscape) interface to apply various forest growth and fire behavior models within an ArcGIS? platform to design and test fuel treatment alternatives. It eliminates a number of tedious data transformations and repetitive processes that have plagued the fire operations and research communities as they apply the models to solve fuel management problems. This User Guide and Tutorial includes an overview of ArcFuels and its functionality, a tutorial highlighting all the tools within ArcFuels, and fuel treatment planning scenarios. There is also a section for obtaining, formatting, and setting up ArcFuels for use with your own data. It is assumed that the reader has basic familiarity with the Forest Vegetation Simulator (forest growth and yield program) and FlamMap (landscape fire behavior model).