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Abstract Forest characteristics, structure, and dynamics within the North American boreal region are heavily influenced by wildfire intensity, severity, and frequency. Increasing temperatures are likely to result in drier conditions and longer fire seasons, potentially leading to more intense and frequent fires. However, an increase in deciduous forest cover is also predicted across the region, potentially decreasing flammability. In this study, we use an individual tree-based forest model to test bottom-up (i.e. fuels) vs top-down (i.e. climate) controls on fire activity and project future forest and wildfire dynamics. The University of Virginia Forest Model Enhanced is an individual tree-based forest model that has been successfully updated and validated within the North American boreal zone. We updated the model to better characterize fire ignition and behavior in relation to litter and fire weather conditions, allowing for further interactions between vegetation, soils, fire, and climate. Model output following updates showed good agreement with combustion observations at individual sites within boreal Alaska and western Canada. We then applied the updated model at sites within interior Alaska and the Northwest Territories to simulate wildfire and forest response to climate change under moderate (RCP 4.5) and extreme (RCP 8.5) scenarios. Results suggest that changing climate will act to decrease biomass and increase deciduous fraction in many regions of boreal North America. These changes are accompanied by decreases in fire probability and average fire intensity, despite fuel drying, indicating a negative feedback of fuel loading on wildfire. These simulations demonstrate the importance of dynamic fuels and dynamic vegetation in predicting future forest and wildfire conditions. The vegetation and wildfire changes predicted here have implications for large-scale changes in vegetation composition, biomass, and wildfire severity across boreal North America, potentially resulting in further feedbacks to regional and even global climate and carbon cycling.
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The predictability of near‐term forest biomass change in boreal North America
Abstract Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.
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- PAR ID:
- 10494137
- Publisher / Repository:
- ESA Journals
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/ecs2.4737
