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1. Coupled fire-atmosphere-smoke forecasting: current capabilities and plans for the future

   https://doi.org/10.14195/978-989-26-16-506_104  

   Kochanski, Adam K.; Mandel, Jan; Vejmelka, Martin; Burke, Dalton; Hearn, Lauren; Haley, James; Farguell Caus, Angel; Schranz, Sher (August 2018, Advances in Forest Fire Research 2018)

   Viegas, Domingos Xavier (Ed.)

   In this paper, we present an integrated wildland fire forecasting system based on combining a high resolution, multi-scale weather forecasting model, with a semi-empirical fire spread model and a prognostic dead fuel moisture model. The fire-released heat and moisture impact local meteorology which in turn drives the fire propagation and the dead fuel moisture. The prognostic dead fuel moisture model renders the diurnal and spatial fuel moisture variability. The local wind and the fuel moisture variation drive the fire propagation over the landscape. The sub-kilometer model resolution enables detailed representation of complex terrain and small-scale variability in surface properties. The fuel moisture model assimilates surface observations of the 10h fuel moisture from Remote Automated Weather Stations (RAWS) and generates spatial fuel moisture maps used for the fire spread computations. The dead fuel moisture is traced in three different fuel classes (1h, 10h and 100h fuel), which are integrated at any given location based on the local fuel description, to provide the total dead fuel moisture content at the fire-model grid, of a typical resolution of tens of meters. The fire simulations are initialized by a web-based control system allowing a user to define the fire anywhere in CONUS as well as basic simulation properties, such as simulation length, resolution, and type of meteorological forcing for any time meteorological products are available to initialize the weather model. The data is downloaded automatically, and the system monitors execution on a cluster. The simulation results are processed while the model is running and displayed as animations on a dedicated visualization portal. 

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2. Simulating the Potential for Invasive Grass Expansion to Alter Wildfire Behavior in Southern California With WRF‐Fire

   https://doi.org/10.1029/2024JG008574  

   Wang, Bowen; Madakumbura, Gavin_D; Juliano, Timothy_W; Williams, A_Park (August 2025, Journal of Geophysical Research: Biogeosciences)

   Abstract Invasion by non‐native annual grasses poses a serious threat to native vegetation in California, facilitated through interaction with wildfires. Our work is the first attempt to use the coupled fire‐atmosphere model, WRF‐Fire, to investigate how shifts from native, shrub‐dominated vegetation to invasive grasses could have affected a known wildfire event in southern California. We simulate the Mountain Fire, which burned >11,000 ha in July 2013, under idealized fuel conditions representing varying extents of grass invasion. Expanding grass to double its observed coverage causes fire to spread faster due to the lower fuel load in grasses and increased wind speed. Beyond this, further grass expansion reduces the simulated spread rate because lower heat release partially offsets the positive effects. Our simulations suggest that grass expansion may generally promote larger faster‐spreading wildfires in southern California, motivating continued efforts to contain and reduce the spread of invasive annual grasses in this region. 

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3. Do Vegetation Fuel Reduction Treatments Alter Forest Fire Severity and Carbon Stability in California Forests?

   https://doi.org/10.1029/2023EF003763  

   Daum, Kristofer L; Hansen, Winslow D; Gellman, Jacob; Plantinga, Andrew J; Jones, Charles; Trugman, Anna T (March 2024, Earth's Future)

   Abstract Forest fire frequency, extent, and severity have rapidly increased in recent decades across the western United States (US) due to climate change and suppression‐oriented wildfire management. Fuels reduction treatments are an increasingly popular management tool, as evidenced by California's plan to treat 1 million acres annually by 2050. However, the aggregate efficacy of fuels treatments in dry forests at regional and multi‐decadal scales is unknown. We develop a novel fuels treatment module within a coupled dynamic vegetation and fire model to study the effects of dead biomass removal from forests in the Sierra Nevada region of California. We ask how annual treatment extent, stand‐level treatment intensiveness, and spatial treatment placement alter fire severity and live carbon loss. We find that a ∼30% reduction in stand‐replacing fire was achieved under our baseline treatment scenario of 1,000 km² year⁻¹after a 100‐year treatment period. Prioritizing the most fuel‐heavy stands based on precise fuel distributions yielded cumulative reductions in pyrogenic stand‐replacement of up to 50%. Both removing constraints on treatment location due to remoteness, topography, and management jurisdiction and prioritizing the most fuel‐heavy stands yielded the highest stand‐replacement rate reduction of ∼90%. Even treatments that succeeded in lowering aggregate fire severity often took multiple decades to yield measurable effects, and avoided live carbon loss remained negligible across scenarios. Our results suggest that strategically placed fuels treatments are a promising tool for controlling forest fire severity at regional, multi‐decadal scales, but may be less effective for mitigating live carbon losses. 

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4. Fuel Loads and Plant Traits as Community‐Level Predictors of Emergent Properties of Vulnerability and Resilience to a Changing Fire Regime in Black Spruce Forests of Boreal Alaska

   https://doi.org/10.1029/2021JG006696  

   Grzesik, E_J; Hollingsworth, T_N; Ruess, R_W; Turetsky, M_R (March 2022, Journal of Geophysical Research: Biogeosciences)

   Abstract Black spruce forest communities in boreal Alaska have undergone self‐replacement succession following low‐to‐moderate severity fires for thousands of years. However, recent intensification of interior Alaska's fire regime, particularly deeper burning of the soil organic layer, is leading to shifts to deciduous‐dominated successional pathways, resulting in many socioecological consequences. Both fuel load quantity and quality (or “burnability”) influence black spruce plant communities' potential to burn. Even relatively low fuel loads, such as those seen in black spruce forest understory, can be highly influential drivers of fire behavior due to their high flammability. Additionally, black spruce community self‐replacement following fire can be largely attributed to the suite of functional and life history traits possessed by the species dominating these communities. We used fuel load (quantity and quality) and amount of within‐population plant trait variation (coefficient of variation; CV) as community‐level emergent properties to investigate black spruce forest vulnerability and resilience to a changing fire regime across the landscape. Our burn severity potential index (BSPI), calculated from fuel load quantity and quality measurements, indicates that drier, higher elevation stands with thicker active layers were the most vulnerable to fire‐induced vegetation shifts under a changing fire regime. Forest resilience to fire‐induced vegetation shift, represented by higher CV, was negatively associated with BSPI and greatest in ecoregions dominated by lowland black spruce forests. Together, these analyses provide critical information for determining the likelihood of stand‐replacing shifts in dominant vegetation following fire and for implementing appropriate ecosystem management practices. 

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5. Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem

   https://doi.org/10.3390/fire6040151  

   Loudermilk, Eva Louise; Pokswinski, Scott; Hawley, Christie M.; Maxwell, Aaron; Gallagher, Michael R.; Skowronski, Nicholas S.; Hudak, Andrew T.; Hoffman, Chad; Hiers, John Kevin (April 2023, Fire)

   Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models. 

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