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

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 v...

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Published in:Landscape ecology Vol. 35; no. 2; pp. 293 - 318
Main Authors: Povak, Nicholas A., Kane, Van R., Collins, Brandon M., Lydersen, Jamie M., Kane, Jonathan T.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.02.2020
Springer Nature B.V
Subjects:
algorithms
autocorrelation
Biomedical and Life Sciences
California
Ecology
environmental factors
Environmental Management
Extreme weather
fire severity
fire spread
fire weather
Forest management
Fuels
fuels (fire ecology)
Heterogeneity
land ownership
Landscape Ecology
Landscape/Regional and Urban Planning
landscapes
Life Sciences
mountains
National forests
National parks
Nature Conservation
prediction
Predictive control
Research Article
Restoration
Spatial analysis
Spatial dependencies
Stanislaus National Forest
Sustainable Development
Topography
Variability
Variance
Weather
Wildfires
United States > US
California
Stanislaus National Forest
Rim fire
Fire severity
Spatial autocorrelation
Variable importance
Variance partitioning
Machine learning
ISSN:0921-2973, 1572-9761
Online Access:Get full text
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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.
AbstractList 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.
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.
ContextAs 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.ObjectivesIdentify the biophysical and management drivers of severity patterns and their spatial variability across the 2013 Rim Fire, Sierra Nevada, California, USA.MethodsRandom 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.ResultsHigh 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.ConclusionsWildfires 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.
Author Kane, Van R.
Collins, Brandon M.
Kane, Jonathan T.
Povak, Nicholas A.
Lydersen, Jamie M.
Author_xml – sequence: 1
  givenname: Nicholas A.
  orcidid: 0000-0003-1220-7095
  surname: Povak
  fullname: Povak, Nicholas A.
  email: nicholas.povak@usda.gov
  organization: USDA Forest Service, Pacific Northwest Research Station, Wenatchee Forestry Sciences Lab, Oak Ridge Institute for Science and Education (ORISE)
– sequence: 2
  givenname: Van R.
  surname: Kane
  fullname: Kane, Van R.
  organization: School of Environmental and Forest Sciences, University of Washington
– sequence: 3
  givenname: Brandon M.
  surname: Collins
  fullname: Collins, Brandon M.
  organization: Center for Fire Research and Outreach, University of California
– sequence: 4
  givenname: Jamie M.
  surname: Lydersen
  fullname: Lydersen, Jamie M.
  organization: Department of Environmental Science, Policy and Management, University of California, California Department of Forestry and Fire Protection, Fire and Resource Assessment Program
– sequence: 5
  givenname: Jonathan T.
  surname: Kane
  fullname: Kane, Jonathan T.
  organization: School of Environmental and Forest Sciences, University of Washington
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ContentType Journal Article
Copyright This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019
Landscape Ecology is a copyright of Springer, (2019). All Rights Reserved.
Copyright Springer Nature B.V. Feb 2020
Copyright_xml – notice: This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019
– notice: Landscape Ecology is a copyright of Springer, (2019). All Rights Reserved.
– notice: Copyright Springer Nature B.V. Feb 2020
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ISSN 0921-2973
IngestDate Fri Sep 05 09:45:59 EDT 2025
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Sat Oct 18 22:49:24 EDT 2025
Tue Nov 18 22:31:32 EST 2025
Sat Nov 29 03:12:03 EST 2025
Fri Feb 21 02:33:18 EST 2025
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Issue 2
Keywords Rim fire
Fire severity
Spatial autocorrelation
Variable importance
Variance partitioning
Machine learning
Language English
LinkModel DirectLink
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PublicationPlace Dordrecht
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PublicationTitle Landscape ecology
PublicationTitleAbbrev Landscape Ecol
PublicationYear 2020
Publisher Springer Netherlands
Springer Nature B.V
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Snippet Context As the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by...
ContextAs the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by...
CONTEXT: As the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by...
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SubjectTerms algorithms
autocorrelation
Biomedical and Life Sciences
California
Ecology
environmental factors
Environmental Management
Extreme weather
fire severity
fire spread
fire weather
Forest management
Fuels
fuels (fire ecology)
Heterogeneity
land ownership
Landscape Ecology
Landscape/Regional and Urban Planning
landscapes
Life Sciences
mountains
National forests
National parks
Nature Conservation
prediction
Predictive control
Research Article
Restoration
Spatial analysis
Spatial dependencies
Stanislaus National Forest
Sustainable Development
Topography
Variability
Variance
Weather
Wildfires
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Title Multi-scaled drivers of severity patterns vary across land ownerships for the 2013 Rim Fire, California
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