Wildfire Impact Modeling Assessing Threat, Economic Exposure and Return on Investment for Mitigation Planning

1
Wildfire Impact Modeling Assessing Threat,
Economic Exposure and Return on Investment for
Mitigation Planning

• David Buckley, Sanborn Map Company
• Jason Batchelor, County of San Diego, Department
  of Planning and Land Use
• Joe Berry, W.M. Keck Visiting Scholar in
  Geosciences, Geography, University of Denver and
  Principal, Berry Associates // Spatial
  Information Systems (BASIS)
• (presenter and corresponding author
  jberry_at_innovativegis.com)

2

Abstract   Wildfires are a growing problem across
the Nation as climate change extends our fire
seasons. Coupled with a historical policy of
aggressively fighting fires this has resulted in
a buildup of volatile vegetation and fuels. In
addition, the consequence of wildfires has never
been greater as the trend continues of people
moving into the wildland. The Wildland Urban
Interface (WUI) is the combat zone for wildfires
with significant potential social, economic and
environmental impacts. With urban growth and
expansion into wildland areas there has been a
significant increase in risk to people and their
homes. Fire professionals in all agencies are
challenged with how to reduce the risk to
wildfire in the WUI, while responsibly allocating
budgets to high priority areas for preparedness
planning and response and suppression. It is
clear that there is an increasing need for fuel
treatments, mitigation planning, and prevention
to reduce the risk to communities in the WUI. To
date, the lack of consistent, accurate
information limits the success of preparedness
planning. There is a general lack of reliable
information to support decision making
including mitigation planning, prevention and
response and suppression. Fire planners are
challenged to quantify the risk to communities
and prioritize mitigation efforts to best protect
people and their homes. In addition, with the
current economic situation, there is an
increasing demand to document accomplishments and
performance measures what is the effect of our
mitigation efforts, and are we spending our
budgets efficiently? This presentation focuses
on the utility of geotechnology and map analysis
procedures in the assessment and planning
process, not only for identifying areas of
greatest risk, but also in quantifying the dollar
impact of wildfire and proposed mitigation
efforts. This includes development of four
distinct modeling phases 1) Wildfire Threat
defining the probability of a wildfire based on
the integration of historical weather and
occurrence data with fire behavior models, 2)
Community Wildfire Risk Assessment that
identifies highly vulnerable communities, 3)
Economic Exposure identifying estimated loss by
integrating threat with economic data, and 4)
Return on Investment for evaluating different
mitigation alternatives. The assessment
methodologies can be applied at both strategic
and tactical scales. These tools not only
provide the basis for more informed decision
making, but they also provide a consistent basis
for funding allocation. The paper describes
results and lessons learned in applying these
wildfire modeling approaches in San Diego County,
CA.
3
Living with Wildfire (Wildland/Urban Interface)
4
Wildfire Impact (San Diego County impacts)
5
Risk Model Overview
6
Wildfire Risk Modeling (advanced map analysis
application)
Wildfire Risk Modeling utilizes map analysis
procedures to predict Threat based on wildfire
behavior considering Topography, Weather,
Historical Fire Occurrence and Fuel Loading

• that can be used to assess Impacts, such as
• Identifying Communities-at-Risk
• Economic Exposure EE Threat Value, and
• Mitigation Return on Investment ROIm
  (EEBefore EEAfter) / Treatment

7
Technology/Science Integration (beyond mapping)
Wildfire Impact Modeling extends traditional
Threat Mapping to Impact Assessment
Domain Expertise
Data Expertise
Solutions Expertise
integration of science and technology
8
Technical Approach (fire behavior modeling)

• The Threat portion of the
• Risk Model integrates
• historical weather
• fire ignition data
• surface and canopy fuels
• fire behavior analysis
• fire effects and suppression
• effectiveness

• Threat describes the probability of fire
  occurring using methods that are
• Quantifiable
• Repeatable
• Comparable across
  time and space

• The process has been thoroughly
  peer-reviewed/published and used in Wildfire Risk
  Mapping of most of the western and southern
  states in the U.S.

dynamic spread not a simple data sandwich
of conditions at a location
9
Technical Approach (model flowchart)
10
Data Requirements
11
Data Requirements (four critical spatial
considerations)
12
Satellite Imagery Use (fuels mapping)
13
Mapping Resolution Comparison (Landsat 30m)
14
Mapping Resolution Comparison (Ikonos 1m)
Fuels Mapping occurs at different resolutions
(spatial, thematic, temporal) based on the level
of detail of the assessment, and purpose of the
dataset most often Wildland (30m) Urban
Interface (4m/1m)
15
Integrating LANDFIRE with Current Risk Model
16
Example OF WILDFIRE Threat Assessment Outputs

• Colorado Wildfire Threat

17
Wildfire Threat (Colorado Example)

• Wildfire Threat is mapped at a 30m resolution for
  the burnable areas within the state
• Delivered via a custom ArcGIS desktop application
  - WFRAS
• Demo of web mapping application for 2 counties

18
Wildfire Threat (Boulder area)

• A consistent 9 interval scale from Very Low to
  Extreme threat is used for burnable areas
• Note the two distinct high threat areas
• interpretation?

19
Wildfire Threat (Durango Area)

• Note the more dispersed pattern of high threat
  areas
• interpretation?

20
Visualization on the Web (Virtual Earth)
21
Using Risk ASSESSMENT Outputs to characterize
communities

• Southern Communities-at-Risk Example

22
Coastal Communities (South Carolina)
23
Wildfire Threat

• Community boundaries shown overlaid with Wildfire
  Threat
• Note the area of extreme risk shown by the dashed
  red ellipse
• Graphic (Visualization)

24
Determining Community-at-Risk Ratings

• Wildfire Threat values within a 3 mi. proximity
  buffer of each community was used to derive CAR
  ratings
• average Threat value for each community
• (Graphic Summary)

25
Community-at-Risk Statistics (South Carolina)
Level Number of Communities Percent Communities
Low 26 0.3
Moderate 3,265 34.4
High 6,129 64.6
Very High 72 0.8
Total 9,492 100.00
Wildfire Threat Summary by Communities
26
Risk Ratings by Watersheds (El Dorado County, CA)
Wildfire Threat Summary by Watersheds
Ecological Communities
27
PUTTING ASSESSMENT RESULTS TO WORK

• Quantifying Economic Impacts

28
Evaluating Wildfire Impact

• Social Impacts
• Communities-at-Risk
• Potential loss of life (public safety)
• Low capacity communities (minimal resources)
• Employment and assistance
• Environmental Impacts
• Environmentally sensitive areas
• Threatened and endangered species (veg
  wildlife)
• Economic Impacts
• Loss of structures and property
• Damage to critical facilities and infrastructure
• Loss to commodity agriculture

29
Economic Impact Metrics (mapping loss)
30
Economic Impact Analysis (underlying theory)
In general Risk Modeling theory, Risk is the
product of Probability (Threat likelihood) and
the Impact/Consequences of a Hazard
Threat x Value Exposure

• Quantify the Dollar Exposure (economic impact)
• Quantify the ROI for Mitigation (treatments)

31
Economic Impact Analysis (conceptual flowchart)

• Dollar Exposure
• Damage Exposure

32
Example Economic Analysis Output (San Diego
County, CA)

• Quantifying Wildfire
• 1) Dollar Exposure
• 2) Damage Exposure

33
1) Dollar Exposure (Value to Rebuild Structures)

• Value
• Using County assessor data the value of
  structures is identified as the cost to rebuild
• This is done on a parcel-by-parcel basis

34
1) Dollar Exposure (Wildfire Threat)

• Wildfire Threat
• Probability that a wildfire will occur at any
  location

35
1) Dollar Exposure (Calculate Dollar Exposure by
Area)
Dollar Exposure
36
1) Dollar Exposure (Calculate Dollar Exposure by
Land Use)

• Outputs can also be subtotaled by Land Use Zones
  to provide more detail

37
2) Damage Exposure (ROI Calculations for
Mitigation)

• Using analysis tools to evaluate the impact of
  fuel treatments on potential loss of structures
• Simulate fuel treatments
• Calculate the change in vegetation fuels
• Calculate the change in wildfire threat
• Calculate the change in dollar exposure (economic
  impact of treatment)
• Determine the ROI for mitigation fuel treatments

38
2) Damage Exposure (Calculate Risk before
Mitigation Investment)

• Risk BEFORE fuel treatments
• Primary areas of major risk in the hilly terrain
  on the southern edge of the community

39
2) Damage Exposure (Calculate Risk before
Mitigation Investment)

• Risk AFTER Fuel treatments
• Substantial reduction in threat after the
  treatments
• now recalculate the dollar exposure

40
2) Damage Exposure (Calculate Risk before
Mitigation Investment)
BEFORE AFTER
Rebuild Exposure 42 m 34 m

• Rebuild Exposure 34m after treatment
• Substantial reduction in exposure
• 8m

41
Concluding Comments
Modeling of Wildfire Impact (Economic, Social and
Environmental) is gaining interest particularly
by non-traditional parties and stakeholders
concerned with policy, planning, mitigation and
recovery as well as suppression
Spatially explicit modeling with high resolution
(spatial, thematic, temporal, mapping and
modeling) will be instrumental in understanding
and addressing potential/actual wildfire impacts
as