A Fire Hazard Decision Support Tool for Improved Rural Security

1
A Fire Hazard Decision Support Tool for
Improved Rural Security

• Chris McColl
• Center for Spatial Information
• Central Washington University

2
Overview

• Current wildland fire and land use policies
• Decision support via model integration
• Objectives model review
• Case study application
• Summary of Benefits

3
Government Policy

• National Fire Plan - Promote community assistance
  by supporting local governments in the
  implementation of fire-sensitive land use
  planning (USDA-FS, 2001).
• EPA Smart Growth Comprehensive Land Use
  Planning (USEPA, 2003)
• Opportunity to integrate these land use planning
  mandates?
• Incorporate wildfire sensitivity into
  comprehensive land use plans - thereby address
  aspects of National Fire Plan and the EPAs
  principles of smart growth simultaneously.

Tripod Complex Wildfire, WA, 2006 175,000 acres
burned. Source USFS, 2006.
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Project Objectives

• Develop method to incorporate wildland fire
  hazard considerations within comprehensive land
  use policy development
• Integration of a fire behavior model with a land
  use forecasting model
• Models and methodology should be accessible to
  local governments with limited budgets
• Methods should be replicable and have the ability
  to conduct alternative scenario analysis
• Ability to promote and support transparent
  decision making processes.

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Models and Data
Bryams Fireline Intensity (kW/m) used as a
surrogate to define potential wildland fire
hazard Selected because (a) it
incorporates rate of spread and heat per unit
area (b) it has meaningful fire
suppression interpretations (Pyne, Andrews,
Laven, 1996 Platt et al.,
2006). Classification 0 Very Low
Potential Wildland Fire Hazard 1-346
Low Potential Wildland Fire Hazard fire can be
attacked by hand 346-1730 High Potential
Wildland Fire Hazard fires should not be
attacked by hand control
efforts at the head of the fire may not always be
effective gt1730 Extreme Potential
Wildland Fire Hazard Fire may exhibit spotting,
crowning, and torching.

• FlamMap 3.0 (Finney, 2006) Fire Behavior Model
  that creates raster maps of
• Potential fire behavior characteristics
• Rate-of-spread
• Flame length
• Crown fire activity
• Fireline intensity
• Heat per unit area
• Data Inputs
• Topographic data
• Elevation (DEM)
• Slope
• Aspect
• Fuels data
• Fire behavior fuel model (FBFM 13 or 40)
• Stand height
• Canopy base height
• Canopy bulk density
• Canopy cover
• Meteorological data Wind speed/direction data
  Fuel Moisture Data Files

Landfire Project www.landfire.gov
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Land Use Model Selection

• Reviewed multiple models California Urban Model
  II (CUFII), Uplan, SLEUTH, UrbanSim, and What If?
• What If? Land Use Forecasting (1999, R.
  Klosterman)
• Selected for
• Ease of use
• Data resource flexibility
• Transparency
• Ability to educate, inform, and include community
• Does not attempt to exactly predict future
  conditions.
• It is an explicit policy-oriented planning tool.

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Model Integration Workflow
Landfire data

• Fire Behavior Output data
• Rate-of-spread
• Flame length
• Crown fire activity
• Heat per unit area
• Fireline intensity

Potential Wildfire Hazard Layer
FlamMap 3.0
Meteorological data
What If? Forecasted Residential Growth

• LU Forecasting Data Input Layers
• Potential Wildfire Hazard
• FEMA Flood Extents
• Proximity to Transportation Arteries
• Slope
• Right of Way
• Ag. Forest Land Preservation
• Private/Public Lands
• Existing Development
• Proximity to fire stations

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Case study application Kittitas County, WA.
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Study Site Information

• Fire behavior fuel model statistics
• Wind data
• Used 10 yr average of max. wind speed data from
  RAWS and local weather stations
• Fuel Moisture File data
• 10 yr average of 1-hr, 10-hr, 100-hr, woody and
  herbaceous provided by WADNR National Weather
  Service

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Development of Kittitas Wildfire Hazard Map
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Land Use Data Sets

• What If?
• Kittitas County comprehensive zoning plan
  (11001400)
• Kittitas County tax parcel data (11001400)
• USGS DEM (10 meter)
• Hydrology data layer (National Hydrography
  Dataset, 1100,000)
• Kittitas County Roads data layer (1100)
• FEMA flood plain data layer (124,000)
• U.S. Census (198019902000)
• GMA growth projections (Low and High)
• Application
• Tested one specific comprehensive land use policy
  
• Forecasted low-density residential growth using
  both a low and high growth rate (lower and upper
  bound of land use distribution)

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Land use modeling assumptions

• Suitability Factors
• Slope, Proximity to highway, Agricultural lands,
  Floodplains, Right of ways, Private/public Lands,
  Fire station proximity, Wildland fire hazard.
• Growth Assumptions
• Low-density residential growth would occur at the
  same rate as Kittitas County new lots would on
  average consume 1 acre initial housing counts
  based on parcel data.
• Allocation
• Most suitable land determined by the suitability
  analysis are allocated first.
• Allocated for years 2015, 2025, and 2050.
• Growth Constraints
• Growth limited on Ag. and Commercial forested
  lands focused within 2 km of main transportation
  routes Rangeland and Rural regions targeted for
  growth.

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Effects of Wildfire Hazard Data Inclusion on Land
Use Forecasting Scenarios
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Urban Sprawl Evaluation

• Forecasted parcel development pattern may be
  unclear,
• thus difficult to assess if smart growth
  objectives are being met.
• Density analysis of forecasted growth provides
  useful visual tool.
• Polygons represent regions forecasted to receive
  growth in 2050
• at a density greater than 100 units/sq. mile

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Summary of Benefits

• Direct inclusion of wildfire hazard
  considerations within planning process
• Provide ability to balance various (sometimes
  competing) planning objectives
• Ability to quantitatively and qualitatively
  discern impacts of various policy alternatives
• Methodology is one that can be implemented at
  local government scale (i.e. affordable,
  replicable, accessible)
• Process provides opportunities to educate,
  inform, and include citizens
• Addresses aspects of both the National Fire Plan
  and Smart Growth principles embodied in WA State
  Growth Management Act.

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Acknowledgements

• Center for Spatial Information at Central
  Washington University www.cwu.edu/csi
• RGIS (Rural Geospatial Innovations)
  www.ruralgis.org
• USDA-CSREES www.csrees.usda.gov

CSI Center for Spatial Information
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References

• Cova, T.J., Sutton, P.C., Theobald, D.M. (2004).
  Exurban change detection in fire-prone areas with
  nighttime satellite imagery. Photogrammetric
  Engineering and Remote Sensing 70, 1249-1257.
• Finney, M.A. (2006). An overview of FlamMap fire
  modeling capabilities. USDA Forest Service
  Proceedings RMRS-P-41.
• Haight, R.G., Cleland, D.T., Hammer, R.B.,
  Radeloff, V.C., and Rupp, T.S. (2004). Assessing
  fire risk in the wildland-urban interface.
  Journal of Forestry 103 (7), pp. 41-47.
• Klosterman, R. E. (1999). The What If?
  collaborative support system. Environment and
  Planning, B Planning and Design, 26, 393-408.
• Mote, P. W., Parson, E. A., Hamlet, A. F.,
  Keeton, W. S., Lettenmaier, D., Mantua, N., et
  al. (2003). Preparing for climatic change The
  water, salmon, and forests of the Pacific
  Northwest. Climatic Change, 61, 45-88.
• Radeloff, V.C., Hammer, R.B., Stewart, S.I.,
  Fried, J.S., Holcomb, S.S., McKerry, J.F. (2005).
  The Wildland-Urban Interface in the United
  States. Ecological Applications 15(3), 799-805.
• Platt, R.V. (2006). A model of exurban land-use
  change and wildfire mitigation. Environment and
  Planning B Planning and Design 33, 749-765.
• Platt, R.V., Veblen, T.T., Sherriff, R.L. (2006).
  Are wildfire mitigation and restoration of
  historic forest structure compatible? A spatial
  modeling assessment. Annals of the Association of
  American Geographers 96 (3), pp. 455-470.
• Parsons E. A., Mote, P.W., Hamlet, A., Mantua,
  N., Snover, A., Keeton, W., Miles, E., Canning,
  D., Ideker, K.G. (2001). Chapter 9 Potential
  consequences of climate variability and change
  for the Pacific Northwest. In National
  Assessment Synthesis Team (Eds.) The potential
  consequences of Climate variability and change
  (pp. 247-280). Cambridge, UK Cambridge
  University Press.
• Pyne, S.J., Andrews, P.L., and Laven, R.D.
  (1996). Introduction to wildland fire. New York
  Wiley.

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• Thank you

Questions?
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Wildland Fire and Urban Development Trends

• The rapid expansion of human development into
  previously wildland areas has significantly
  increased.
• 1982-1992 to 1992-2001, the annual rate of land
  conversion from rural to urban uses has nearly
  doubled (USDA-NRCS, 2003).
• Uninhabited forests and rangelands with limited
  values-at-risk now contain homes, communities,
  and associated infrastructure.
• Incidents of wildland fires represent a
  substantial threat to the security of rural
  communities who border large expanses of
  open-lands, rich in wildfire fuels (i.e. WUI
  areas), (Haight et al., 2004).
• Climate change is projected to generate hotter
  and dryer conditions for Pacific Northwest, thus
  raising the threat of severe wildland fire events
  (Parsons et al., 2001 Mote et al., 2003).

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Rural Geospatial Innovations (RGIS)

• Mission
• The mission of RGIS is to eliminate the digital
  divide facing rural America by promoting the
  transfer of geospatial technologies to
  under-served rural areas by
• providing geospatial tools, technologies, and
  training to empower local governments,
  organizations, and citizens to understand and
  participate in decisions that affect their
  environment, economy, and quality of life
• educating and training a cadre of people to apply
  geospatial technologies to rural issues

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Landfire Seamless Data Download IMS
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What If? Land use forecasting

• It is an interactive GIS-based system that
  supports many aspects of the land use planning
  process including
• Land suitability analyses
• Growth Analysis (future land use demand)
• Allocating land use demand to suitable locations
• Does not attempt to exactly predict future
  conditions.
• It is an explicit policy-oriented planning tool.
• Used to determine what would happen if clearly
  defined policy choices are made and assumptions
  concerning the future prove to be correct

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Kittitas County Background and Fire Statistics

• Since 1973, there have been 1489 fires, with 76.5
  of firesgt100acres
• occurring in the second half of this time span.
• Percentage classified as High Hazard WUI 33
• Growth of land parceled off for resident growth
  from 2001-2006
• Year Growth
• 2001-2006 6566 new parcels
• 3843 / 6566 59 fall within the WUI high risk
  category.