Disentangling how climate change and forest management alter fuels and fire regimes in Sierra Nevada forests

Extreme wildfires are increasing in forests around the globe and releasing billions of tons of previously sequestered carbon into the atmosphere every year. In the California Sierra Nevada, wildfires have become more extreme in response to both climate change and historical fire suppression. To address these changes, forest managers use fuel treatments to reduce fire hazard, return fire regimes to their historical range of variability, and promote stable forest carbon. These treatments include a range of mechanical (e.g., thinning, pruning, mastication) and prescribed fire (e.g., broadcast burns, pile burns) approaches. However, it remains unclear how fire regimes will change under future climate-and when, where, and what fuel treatments will be most effective in the face of those changes.Simulation models provide a tool for projecting how climate change will influence future fire regimes, the extent to which shifting fire regimes will release carbon to the atmosphere, and when and where we can mitigate these changes through fuel treatments. Models provide a framework that enables us to replicate and evaluate the factors that influence fire regimes and postfire carbon dynamics across complex landscapes. However, models are subject to a range of uncertainties and limitations that must be quantified and addressed to make reliable predictions about the future. In this research, I developed, evaluated, and applied a set of models to investigate how climate change is altering fire regimes in the Sierra Nevada ecoregion and how we can best mitigate those changes through fuel management. In chapter 1 of my dissertation, I demonstrated how multiple time series characteristics vary among downscaled Global Climate Model (GCM) projections for four watersheds in the Sierra Nevada Ecoregion, and how each GCM's time series characteristics vary between watershed and regional scales. Then, using these downscaled GCMs as forcing data for a biophysical, fire regime model, I examined how fire regime projections can vary in response to these temporal and spatial input uncertainties. Finally, I illustrated how these analyses can be used for more robust GCM model selection. Conducting more comprehensive time series analyses enables modelers to more mechanistically link meteorological forcings with biophysical model projections. In chapter 2, I conducted a factorial modeling experiment to investigate how climate change and prescribed fire influence future fire regimes and carbon retention in a high-elevation, mixed conifer-dominated watershed in the Sierra Nevada Mountains. I found that climate change led to smaller and more frequent fires across the landscape and that fire hazard increased in the most mesic locations of the watershed due to their historically high fuel loads and climate change-driven increases in fuel aridity. Prescribed fire reduced the size of the largest fires, reduced fire hazard in the most mesic locations, and decreased carbon emissions in subsequent wildfires across the watershed. However, in the hottest and driest future climate scenario, climate change-driven increases in fire hazard outstripped decreases generated by prescribed fire. Our findings suggest that alongside other forest management practices, and in accordance with greenhouse gas reductions, prescribed fire can play an important role in managing fire hazard and increasing decadal-scale carbon retention in fire-prone, climate-impacted landscapes. In chapter 3, I quantified the short- and long-term impacts of fuel treatments on both carbon sequestration and fire risk using the Forest Vegetation Simulator (FVS) at large spatial scales. I develop a cloud-based model framework around FVS to spatialize model outputs of total stand carbon and surface fuels at a 30-m resolution along with their associated uncertainties. As a case study for the new model framework, I simulated nine fuel treatment scenarios for the Tahoe Basin and found that topography plays a key role in long-term carbon outcomes and identified potential underestimation of vegetation density. I then identified how parameterization could be improved to increase confidence in vegetation growth estimates.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2024

Author dc:creator

• Cale, Ashley

Advisors dc:contributor.advisor

• Hanan, Erin J
• Sullivan, Ben W

Committee members dc:contributor.committeemember

• Kennedy, Maureen
• Greenburg, Jonathan
• McAfee, Steph

• Biophysical Models
• Carbon Sequestration
• Climate Change ecology
• Global Circulation Models
• Landscape ecology
• Wildfire

Language dc:language.iso

en_US, English

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11259

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11259