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Multi-scaled drivers of severity patterns vary across land ownerships for the 2013 Rim Fire, California

Abstract

Context

As the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by weather, fuels, topography, and management.

Objectives

Identify the biophysical and management drivers of severity patterns and their spatial variability across the 2013 Rim Fire, Sierra Nevada, California, USA.

Methods

Random forest models were developed separately for reburned and fire-excluded (> 80 year) areas within Yosemite National Park (NP) and Stanislaus National Forest (NF). Models included biophysical, past disturbance, and spatial autocorrelation (SA) predictors. Variable importance was assessed globally and locally. Variance partitioning was used to assess pure and shared variance among predictors.

Results

High spatial variability in the relative dominance of predictors existed across burn days and between land ownerships. Fire weather was a dominant top-down control during plume-dominated fire spread days. However, bottom-up controls from fuels and topography created local, fine-scale heterogeneity throughout. Reburn severity correlated with previous severity suggesting strong landscape memory, particularly in Yosemite NP. SA analysis showed broad-scale spatial dependencies and high shared variance among predictors.

Conclusions

Wildfires are inherently a multi-scaled process. Spatial structure in environmental variables create broad-scale patterns and dependencies among drivers leading to regions of similar fire behavior, while local bottom-up drivers generate fine-scaled heterogeneity. Identifying the conditions under which top-down factors overwhelm bottom-up controls can help managers monitor and manage wildfires to achieve both suppression and restoration goals. Restoration targeting both surface and ladder fuels can mediate future fire severity even under extreme weather conditions.

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Acknowledgements

This research was funded through grant #14-1-01-23 of the Joint Fire Sciences program. This project was supported in part by an appointment to the Internship/Research Participation Program at the Pacific Northwest Research Station, U.S. Forest Service, administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and U.S. Forest Service. The authors thank Susan Prichard and Brion Salter for their thoughtful and thorough reviews of the manuscript.

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Povak, N.A., Kane, V.R., Collins, B.M. et al. Multi-scaled drivers of severity patterns vary across land ownerships for the 2013 Rim Fire, California. Landscape Ecol 35, 293–318 (2020). https://doi.org/10.1007/s10980-019-00947-z

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  • DOI: https://doi.org/10.1007/s10980-019-00947-z

Keywords

  • Fire severity
  • Machine learning
  • Variable importance
  • Spatial autocorrelation
  • Variance partitioning
  • Rim fire