A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States
•Employs a probabilistic exposure analysis to identify the likelihood of populated places interacting with wildfire.•Classified at-risk areas according to a risk matrix comprised of population density and burn probability categories.•Risk matrix allows planners and managers a quick way to identify w...
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| Published in: | Landscape and urban planning Vol. 119; pp. 44 - 53 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier B.V
01.11.2013
Elsevier |
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| ISSN: | 0169-2046, 1872-6062 |
| Online Access: | Get full text |
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| Abstract | •Employs a probabilistic exposure analysis to identify the likelihood of populated places interacting with wildfire.•Classified at-risk areas according to a risk matrix comprised of population density and burn probability categories.•Risk matrix allows planners and managers a quick way to identify where the risk is located spatial, and to qualify the driving factors of the risk (population or burn probability or both).•Suggests a number of ways that managers and planners can use this information for decision-making, fuels modifications and residential planning.
Ongoing human development into fire-prone areas contributes to increasing wildfire risk to human life. It is critically important, therefore, to have the ability to characterize wildfire risk to populated places, and to identify geographic areas with relatively high risk. A fundamental component of wildfire risk analysis is establishing the likelihood of wildfire occurrence and interaction with social and ecological values. A variety of fire modeling systems exist that can provide spatially resolved estimates of wildfire likelihood, which when coupled with maps of values-at-risk enable probabilistic exposure analysis. With this study we demonstrate the feasibility and utility of pairing burn probabilities with geospatially identified populated places in order to inform the development of next-generation, risk-based Wildland-Urban Interface (WUI) maps. Specifically, we integrate a newly developed Residentially Developed Populated Areas dataset with a stochastic, spatially-explicit wildfire spread simulation model. We classify residential population densities and burn probabilities into three categories (low, medium, high) to create a risk matrix and summarize wildfire risk to populated places at the county-level throughout the continental United States. Our methods provide a new framework for producing consistent national maps which spatially identifies the magnitude and the driving factors behind the wildland fire risk to populated places. This framework advances probabilistic exposure analysis.for decision support in emergency management, rural and urban community planning efforts, and more broadly wildfire management and policy-making. |
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| AbstractList | Ongoing human development into fire-prone areas contributes to increasing wildfire risk to human life. It is critically important, therefore, to have the ability to characterize wildfire risk to populated places, and to identify geographic areas with relatively high risk. A fundamental component of wildfire risk analysis is establishing the likelihood of wildfire occurrence and interaction with social and ecological values. A variety of fire modeling systems exist that can provide spatially resolved estimates of wildfire likelihood, which when coupled with maps of values-at-risk enable probabilistic exposure analysis. With this study we demonstrate the feasibility and utility of pairing burn probabilities with geospatially identified populated places in order to inform the development of next-generation, risk-based Wildland-Urban Interface (WUI) maps. Specifically, we integrate a newly developed Residentially Developed Populated Areas dataset with a stochastic, spatially-explicit wildfire spread simulation model. We classify residential population densities and burn probabilities into three categories (low, medium, high) to create a risk matrix and summarize wildfire risk to populated places at the county-level throughout the continental United States. Our methods provide a new framework for producing consistent national maps which spatially identifies the magnitude and the driving factors behind the wildland fire risk to populated places. This framework advances probabilistic exposure analysis.for decision support in emergency management, rural and urban community planning efforts, and more broadly wildfire management and policy-making. •Employs a probabilistic exposure analysis to identify the likelihood of populated places interacting with wildfire.•Classified at-risk areas according to a risk matrix comprised of population density and burn probability categories.•Risk matrix allows planners and managers a quick way to identify where the risk is located spatial, and to qualify the driving factors of the risk (population or burn probability or both).•Suggests a number of ways that managers and planners can use this information for decision-making, fuels modifications and residential planning. Ongoing human development into fire-prone areas contributes to increasing wildfire risk to human life. It is critically important, therefore, to have the ability to characterize wildfire risk to populated places, and to identify geographic areas with relatively high risk. A fundamental component of wildfire risk analysis is establishing the likelihood of wildfire occurrence and interaction with social and ecological values. A variety of fire modeling systems exist that can provide spatially resolved estimates of wildfire likelihood, which when coupled with maps of values-at-risk enable probabilistic exposure analysis. With this study we demonstrate the feasibility and utility of pairing burn probabilities with geospatially identified populated places in order to inform the development of next-generation, risk-based Wildland-Urban Interface (WUI) maps. Specifically, we integrate a newly developed Residentially Developed Populated Areas dataset with a stochastic, spatially-explicit wildfire spread simulation model. We classify residential population densities and burn probabilities into three categories (low, medium, high) to create a risk matrix and summarize wildfire risk to populated places at the county-level throughout the continental United States. Our methods provide a new framework for producing consistent national maps which spatially identifies the magnitude and the driving factors behind the wildland fire risk to populated places. This framework advances probabilistic exposure analysis.for decision support in emergency management, rural and urban community planning efforts, and more broadly wildfire management and policy-making. |
| Author | Thompson, Matthew P. Calkin, David E. Haas, Jessica R. |
| Author_xml | – sequence: 1 givenname: Jessica R. surname: Haas fullname: Haas, Jessica R. email: jrhaas@fs.fed.us – sequence: 2 givenname: David E. surname: Calkin fullname: Calkin, David E. email: decalkin@fs.fed.us – sequence: 3 givenname: Matthew P. surname: Thompson fullname: Thompson, Matthew P. email: mpthompson02@fs.fed.us |
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| Cites_doi | 10.1139/x02-068 10.2737/RMRS-GTR-175 10.1007/s00267-011-9796-z 10.2737/RMRS-GTR-119 10.1016/j.foreco.2005.02.010 10.4996/fireecology.0802125 10.1061/(ASCE)1527-6988(2006)7:4(150) 10.1007/s10661-010-1731-x 10.2737/RMRS-GTR-153 10.1071/WF06144 10.1007/s00477-011-0462-z 10.1007/s10708-011-9419-5 10.1007/s10980-009-9398-9 10.1071/WF11001 10.1093/forestscience/35.2.319 10.1890/04-1413 10.1007/s10708-007-9105-9 10.1007/s11069-012-0265-7 10.1071/WF02059 10.1071/WF11060 10.5849/jof.12-027 10.1093/jof/107.2.78 10.1111/j.1539-6924.2012.01911.x 10.2737/RMRS-GTR-220 10.1007/s10666-010-9241-3 10.1016/j.landurbplan.2007.06.002 10.1093/jof/109.5.274 10.1016/j.forpol.2010.07.009 10.1093/jof/103.4.179 10.1071/WF07131 10.1071/WF11117 10.1093/jof/102.7.41 10.1016/j.jenvman.2011.03.015 10.1007/s11069-006-9032-y 10.1071/WF08132 10.1071/WF08185 10.1016/j.foreco.2010.01.032 10.1016/j.foreco.2009.07.051 10.1007/s10694-009-0134-4 10.2737/RDS-2013-0009 |
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| Keywords | Exposure analysis Wildland urban interface Wildfire risk assessment Burn probability modeling Residentially developed populated areas Vegetation fire Urban area Risk analysis Environmental management Modeling |
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| Snippet | •Employs a probabilistic exposure analysis to identify the likelihood of populated places interacting with wildfire.•Classified at-risk areas according to a... Ongoing human development into fire-prone areas contributes to increasing wildfire risk to human life. It is critically important, therefore, to have the... |
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| SubjectTerms | Animal, plant and microbial ecology Applied ecology Biological and medical sciences Burn probability modeling Computer simulation Conservation, protection and management of environment and wildlife data collection Exposure Exposure analysis exposure assessment Fires Forest and land fires Fundamental and applied biological sciences. Psychology General aspects General aspects. Techniques Human human development humans Methods and techniques (sampling, tagging, trapping, modelling...) Phytopathology. Animal pests. Plant and forest protection planning population density Probabilistic methods Probability theory Residentially developed populated areas Risk simulation models United States Weather damages. Fires Wildfire risk assessment Wildfires wildland fire management Wildland urban interface |
| Title | A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States |
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