Research and Publications
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Across all treatment types, forests that underwent fuel treatments had significantly lower fire severity in three of five metrics (crown scorch percent, crown torch percent, and torch height) and 3x more surviving trees.
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As the use of risk products has grown, so has confusion and controversy about how they work, their strengths and limitations, and the distinction between wildfire risk and wildfire hazard. Wildfire risk products vary widely in their design, purpose, and technical details. They may use different definitions of risk, values, data sources, fire metrics and models. This diversity means that risk assessments for the same location can vary by design. This variability does not necessarily indicate that the products are flawed or inaccurate. Rather, it reflects that wildfire risk analysis is a diverse field with evolving techniques that serve different users and applications. As this comparative review shows, there is no universal formula for assessing wildfire risk. For potential users of wildfire risk products, the strength in understanding diverse approaches lies not in direct comparison but in recognizing the unique contributions each makes. It also means that users need to invest time into selecting the right risk product(s) for their specific application.
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Anticipating plausible future ecosystem states is necessary for effective ecosystem management. We use climate analog-based impact models and a co-production process with land managers to project future vegetation changes for the state of Oregon, United States, (2041–2070, RCP 8.5) at a management-relevant spatial resolution (270-m). We explore multiple analog-based methodologies, evaluate analog model performance with contemporary validation, and leverage climate analogs to assess projection uncertainty by quantifying areas where multiple vegetation trajectories are plausible under a single climate scenario. We find that analog-based models performed well at reproducing landscape-level vegetation composition, and moderately well at reproducing vegetation at the pixel level. Our results suggest that 64% of the study area will experience future climate conditions that support different potential natural vegetation types and 59% will experience climates corresponding with different potential plant physiognomic types, compared to reference-period conditions. We project a 60% reduction of mesic conifer-dominated forests with transitions to mixed evergreen forest types. We also project losses to dry forests, cold forests and parklands, with commensurate expansions of shrublands, grasslands, and geographic redistribution of dry forest types. We find that in many areas, several vegetation trajectories are plausible under a single climate scenario. Finally, we provide guidance for using future vegetation projections and uncertainty outputs in management decisions using the Resist-Accept-Direct (RAD) adaptation framework.
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Since the late 1800s, the US government has largely removed Indigenous fire stewardship practices from the landscape by implementing a top-down fire suppression system that criminalized traditional fire practices and denaturalized the role of fire in forested environments. A century of routine fire suppression produced dense, homogenous forests capable of sustaining high-intensity wildfire that exceeds the suppression capabilities of land management organizations in many regions, spurring federal leaders to modify management approaches. As part of this change, numerous federal policies and plans have advocated for further involvement of Native American tribes and incorporation of Indigenous knowledge within management decisions. These initiatives represent opportunities to simultaneously expand tribal burning rights and reduce wildfire risk, but imbalanced power dynamics stemming from the historic and ongoing colonization of tribal nations continue to limit successful collaboration. The nature of these power imbalances is multifaceted, and this paper interrogates the ideological forces that uphold the settler-colonial relationship. We conduct a Critical Discourse Analysis (CDA) to analyze the discourses and frames used by tribal and non-tribal wildfire protection plans (WPPs), noting how different narratives are used to reinforce or contest common perceptions of wildfire and, more broadly, the legitimacy of a fire management system built on wildfire suppression and anti-Indigenous ideologies.
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Extreme wildfire events (EWEs) thereby pose new challenges and limits to managing disaster risk. This refers not only to response operations but also to “conventional” preventive measures such as the creation of buffer zones that may no longer be effective. This paper depicts several limits of conventional wildfire risk management measures towards EWEs and introduces the concept of disaster risk management tipping points (DRM TPs) as critical thresholds that necessitate a revised set of disaster risk management strategies.
Building on a bibliographic review, we depict the novelty of the concept and apply it to selected illustrative examples. We propose that this conceptualization is useful when developing “layered” or diversified risk management approaches for different types of wildfire events including extremes. It may also leverage and shift the discussion around responsibilities in managing risk in terms of public versus individual contributions, the distribution of investments as well as related aspects of justice.
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To understand trends in fire severity and area burned, researchers analyzed satellite images taken before and after each fire in western US forests from 1985 to 2022. They classified areas as high severity when at least 95% of the tree canopy was lost according to a satellite-derived fire severity metric called the Composite Burn Index. To understand climate’s role, they combined three key indicators during the summer fire season (vapor pressure deficit, maximum temperature, and climate water deficit) into a single metric called fire season aridity to capture how hot and dry each fire season was. This same method was used to model future fire conditions, projecting changes in total and high-severity burn areas through the mid-21st century under a 2°C warming scenario.
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Given the large size of landscapes, limited management budgets and diverse (sometimes competing) objectives, it can be extremely difficult to know where and how fuel treatments are best undertaken to reduce wildfire risks. While optimization algorithms can help to navigate such complex decisions, the computational cost of applying simulation-based models for predicting wildfire risk has prevented us from using optimization to guide decision-making. To implement optimization by leveraging ‘metamodelling’ approaches that can efficiently estimate the burn probability outputs of simulation models. We use a simulation-optimization approach that links a burn probability (BP) metamodel with the multi-objective optimization algorithm NSGA-II, to develop fuel treatment plans that optimization the trade-offs between different risk reduction objectives and the area treated (AT) by fuel treatment plans in a South Australian case study area. Optimization improves the reduction in BP per area managed by at least 81–284% when compared with existing approaches in our study area. Optimization develops highly effective fuel treatment plans that balance trade-offs between different BP-based objectives and/or levels of resources available for management. Optimization can improve strategic landscape management and offers the potential to help communities better achieve their risk reduction objectives.
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Human activities are increasing the occurrence of megafires that alter ecological dynamics in forest ecosystems. The objective of this study was to understand the impacts of a 610 km2 megafire on patterns of tree regeneration and herbivory across three forest types (aspen/fir, oak/maple, and pinyon/juniper). Seventeen transect pairs in adjacent burned/unburned forest stands (6 aspen/fir, 5 oak/maple, and 6 pinyon/juniper) were measured. Sapling density, meristem removal, and height were measured across the transect network over a three-year period from 2019 to 2021. Tree species able to resprout from surviving roots (oak and aspen) generally responded positively to fire while species that typically regenerate by seeding showed little post-fire regeneration. Browse pressure was concentrated on deciduous tree species and was greater in burned areas but the effect diminished over the three-year study period. Meristem removal by herbivores was below the critical threshold, resulting in vertical growth over time. Our results indicate that forest regeneration within the megafire scar was generally positive and experienced sustainable levels of ungulate browsing that were likely to result in forest recruitment success.
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Prevailing American wildland fire modelling systems fail to predict fire growth in urban areas due to the absence of burnable urban fuels. This research aims to identify fuel models that optimize fire spread in urban areas relative to a hypothetical fire spread model derived from observations of recent urban fires. A target Rate of Spread (RoS) is derived from observations of seven urban conflagrations to anchor the model to absolute RoS. Exhaustive parameter sweeps are used to identify combinations of fuel variables that result in optimal performance. The target RoS is 0.81 km/h. Parameter sweeps converge on unique sets of fuel parameters including (1) BU0, an unconstrained custom fuel model; (2) BU1, a custom fuel model that operates within the constraints of current US modelling systems; and (3) Anderson Fuel Model 9, a best-performing standard fuel model. Although this approach stretches current modelling systems beyond their intended design, the resultant fuel models provide a necessary stopgap for emergency management until urban-specific fire spread models find their way into operational use.
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Data-driven decision support can help guide sustainable grazing management by providing an accurate estimate of grazing capacity, in coproduction with managers. Here, we described the development of a decision support model to estimate grazing capacity and illustrated its application on two sites in the western United States. For the Montgomery PassWild Horse Territory in California and Nevada, the upper limit estimated in the capacity assessment was 398 horses and the current population was 654 horses. For the Eagle Creek watershed of the Apache-Sitgreaves National Forest of eastern Arizona, the lower end of capacity was estimated at 1560 cattle annually, compared to the current average of 1090 cattle annually. In addition to being spatio-temporally comprehensive, the model provides a repeatable, cost-effective, and transparent process for establishing and adjusting capacity estimates and associated grazing plans that are supported by scientific information, in order to support livestock numbers at levels that are sustainable over time, including levels that are below average forage production during drought conditions. This modeling process acts as a decision support tool because it enables different assumptions to be used and explored to accommodate multiple viewpoints during the planning process.