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Large, severe wildfires continue to burn in frequent-fire adapted forests but the mechanisms that contribute to them and their predictability are important questions. Using a combination of ground based and remotely sensed data we analyzed the behavior and patterns of the 2020 Creek Fire where drought and bark beetles had previously created substantial levels of tree mortality in the southern Sierra Nevada. We found that dead biomass and live tree densities were the most important variables predicting fire severity; high severity fire encompassed 41% of the area and the largest high severity patch (19,592 ha) comprised 13% of total area burned. Areas with the highest amounts of dead biomass and live tree densities were also positively related to high severity fire patch size indicating that larger, more homogenous conditions of this forest characteristic resulted in adverse, landscape-scale fire effects. The first two days of the Creek Fire were abnormally hot and dry but weather during the days of the greatest fire growth was largely within the normal range of variation for that time of year with one day with lower windspeeds. From September 5 to 8th the fire burned almost 50% of its entire area and fire intensity patterns inferred from remotely sensed brightness-temperature data were typical except on September 6th when heat increased towards the interior of the fire. Not only was the greatest heat concentrated away from the fire perimeter, but a significant amount of heat was still being generated within the fire perimeter from the previous day. This is a classic pattern for a mass fire and the high amount of dead biomass created from the drought and bark beetles along with high live tree densities were critical factors in developing mass fire behavior. Operational fire behavior models were not able to predict this behavior largely because they do not include post-frontal combustion and fire-atmosphere interactions. An important question regarding this mass fire is if the tree mortality event that preceded it could have been avoided or reduced or was it within the natural range of variation for these forests? We found that the mortality episode was outside of historical analogs and was exacerbated by past management decisions. The Creek Fire shows us how vulnerable of our current frequent-fire forest conditions are to suffering high tree mortality and offering fuel conditions capable of generating mass fires from which future forest recovery is questionable because of type conversion and probable reoccurring high severity fire.

Collaborative adaptive management (CAM) is a process for making decisions about the environment in the face of uncertainty and conflict. Scientists have a central role to play in these decisions. However, while scientists are well-trained to reduce uncertainty by discovering new knowledge, most lack experience with the means to mitigate conflict in contested situations. To address this gap, we drew from our efforts coordinating a large CAM effort, the Sierra Nevada Adaptive Management Project, to offer advice to our fellow environmental scientists. Key challenges posed by CAM include the confusion caused by multiple institutional cultures, the need to provide information at management-relevant scales, frequent turn-over in participants, fluctuations in enthusiasm among key constituencies, and diverse definitions of success among partners. Effective strategies included a dedication to consistency, a commitment to transparency, the willingness to communicate frequently via multiple forums, and the capacity for flexibility. CAM represents a promising, new model for scientific engagement with the public. Learning the lessons of effective collaboration in environmental management is an essential task to achieve the shared goal of a sustainable future.

Fire models predict fire behavior and effects. However, there is a need to know how confident users can be in forecasts. This work developed a probabilistic methodology based on ensemble simulations that incorporated uncertainty in weather, fuel loading, and model physics parameters. It provided information on the most likely forecast scenario, confidence levels, and potential outliers. It also introduced novel ways to communicate uncertainty in calculation and graphical representation and applied this to diverse wildfires using ensemble simulations of the CAWFE coupled weather–fire model ranging from 12 to 26 members. The ensembles captured many features but spread was narrower than expected, especially with varying weather and fuel inputs, suggesting errors may not be easily mitigated by improving input data. Varying physics parameters created a wider spread, including identifying an outlier, underscoring modeling knowledge gaps. Uncertainty was communicated using burn probability, spread rate, and heat flux, a fire intensity metric related to burn severity. Despite limited ensemble spread, maps of mean and standard deviation exposed event times and locations where fire behavior was more uncertain, requiring more management or observations. Interpretability was enhanced by replacing traditional hot–cold color palettes with ones that accommodate the vision-impaired and adhere to web accessibility standards. [ABSTRACT FROM AUTHOR]

The balance between ecosystem emissions of carbon to the atmosphere and removals from the atmosphere indicates whether ecosystems are exacerbating or reducing climate change. Forest ecosystems in the State of California, USA, contain carbon that reaches the highest densities (mass per unit area) in the world, but it has been unresolved whether California ecosystems currently comprise a net sink or source of carbon. The California Global Warming Solutions Act of 2006 established greenhouse gas reduction targets for fossil fuel-burning sectors and ecosystems, underscoring the importance of tracking ecosystem carbon. Here, we conduct statewide spatial inventories of the aboveground live carbon stocks of forests and other terrestrial ecosystems of California, excluding agricultural and urban areas. We analyzed biomass data from field measurements of the Forest Inventory and Analysis program, published biomass information and remote sensing data on non-forest vegetation, and spatial distributions of vegetation types, height, and fractional cover derived by the Landfire program from Landsat remote sensing at 30m spatial resolution. We conducted Monte Carlo analyses of the uncertainty of carbon stock change estimates from errors in tree biomass estimates, remote sensing, and estimates of the carbon fraction of biomass. The carbon stock in aboveground biomass was 850±230 Tg (mean±95% confidence interval) in 2010. We found a net aboveground live carbon stock change of −69±15 Tg from 2001 to 2010, a rate of change of −0.8±0.2%y−1. Due to slow decay of some dead wood, all of the live carbon stock change does not immediately generate emissions. Wildfires on 6% of the state analysis area produced two-thirds of the live carbon stock loss. This suggests that increased tree densities from a century of fire suppression have allowed the accumulation of fuel for carbon losses in recent wildfires. Remote sensing errors in vegetation classification accounted for most of the uncertainty in the carbon stock change estimates. Improvements are also needed to track spatial patterns of growth and dead wood. Our results establish the beginning of a time series for the state greenhouse gas inventory and provide information on the role of forest conservation and management in California in mitigating global climate change.

Inventories of greenhouse gas (GHG) emissions from wildfire provide essential information to the state of California, USA, and other governments that have enacted emission reductions. Wildfires can release a substantial amount of GHGs and other compounds to the atmosphere, so recent increases in fire activity may be increasing GHG emissions. Quantifying wildfire emissions however can be difficult due to inherent variability in fuel loads and consumption and a lack of field data of fuel consumption by wildfire. We compare a unique set of fuel data collected immediately before and after six wildfires in coniferous forests of California to fuel consumption predictions of the first-order fire effects model (FOFEM), based on two different available fuel characterizations. We found strong regional differences in the performance of different fuel characterizations, with FOFEM overestimating the fuel consumption to a greater extent in the Klamath Mountains than in the Sierra Nevada. Inaccurate fuel load inputs caused the largest differences between predicted and observed fuel consumption. Fuel classifications tended to overestimate duff load and underestimate litter load, leading to differences in predicted emissions for some pollutants. When considering total ground and surface fuels, modeled consumption was fairly accurate on average, although the range of error in estimates of plot level consumption was very large. These results highlight the importance of fuel load input to the accuracy of modeled fuel consumption and GHG emissions from wildfires in coniferous forests. ©2014. American Geophysical Union. All Rights Reserved.

We quantified damage by a microburst windstorm to a northern hardwood forest (Hubbard Brook Experimental Forest, New Hampshire). These storms may be important in regulating the structure and composition of forests of the northeastern United States, but few studies of damage patterns from microbursts have been reported. In the 600 ha area most heavily impacted by the microburst at Hubbard Brook, 4.6% of the canopy was removed. Although most disturbances were small ( Key words: treefall, gap dynamics, thunderstorm, intermediate disturbance, Hubbard Brook Experimental Forest. Nous avons quantifie les dommages causes par une microrafale dans une foret feuillue nordique a la foret experimentale d'Hubbard Brook, au New Hampshire. Ces tempetes peuvent jouer un role important dans le processus de regulation de la structure et de la composition des forets du nord-est des Etats-Unis, mais il existe peu d'etudes des patrons de dommages causes par les microrafales. Dans la zone de 600 ha la plus fortement touchee par la microrafale a Hubbard Brook, 4,6 % du couvert forestier a ete elimine. Bien que la plupart des perturbations aient ete petites ( Mots-cles: chute d'arbres, dynamique des trouees, orage, perturbation intermediaire, foret experimentale d'Hubbard Brook., Introduction For the northern hardwood forests of the northeastern United States (US), vegetation change is dominated by tree-by-tree replacement in small gaps caused by the death of one or a [...]