Tsunami Risk Across Six Communities

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Tsunami Risk Across Six Communities

• 33rd Annual Hazard Research and Applications
  Workshops
• July 14, 2008

NSF Grant HSD 0527387
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Tsunami Risk Across Six Communities

• Bruce Houghton
• Penny Larin
• Liesel Ritchie
• Stephen Meinhold
• Douglas Paton

• Duane Gill
• Chris Gregg
• David Johnson
• Jennifer Horan
• NSF Grant HSD 0527387

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Workshop format

• Introduction
• Awareness Knowledge
• Preparedness
• Social Capital
• Warnings Evacuations
• Wrap up
• Discussion

• Bruce Houghton
• Duane Gill
• Stephen Meinhold Jennifer Horan
• Liesel Ritchie
• Chris Gregg
• Bruce Houghton

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Ocean Shores, WA
Seaside, OR
New Hanover, NC
Coronado, CA
Kauai, HI
Kodiak, AK
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Design and Implementation

• Phase 1 September 2006 June 2007
• 114 closed-end
• 13 open-ended
• Telephone interviews
• Phase 2 October 2007 January 2008
• 49 repeat closed-ended
• 31 new closed-ended

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Sample size and returns
Community P 1 sent P1 returns P2 returns
Oregon 1775 521 213
Washington 1500 582 275
California 2000 666 327
Hawaii 1000 148 66
Alaska 1500 437 167
N. Carolina 1200 448 196
Total 7475 2802 1244
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TOPICS

• Attitudes and beliefs about tsunamis
• Tsunami awareness
• Warning signs of tsunamis
• Preparing for tsunamis
• Tsunami preparedness in the community
• Community values
• General approaches to decision-making

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Past damaging tsunamis
1964
1964
No event
No event
1957
1964
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Tsunami Awareness

• Communities that have experienced a major tsunami
  event with the past 50 years .
• Have greater awareness of the danger, threat, and
  damaging impacts of a future tsunami
• Perceive a greater likelihood of another one
  occurring in the near and distant future

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Tsunami Processes

• The vast majority of respondents know that
  earthquakes are the most common cause of tsunamis
• About 6 out of 10 respondents understand that
  tsunamis can occur as multiple big and small
  waves
• About 3 out of 10 respondents do not know the
  time between waves

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Selected tsunami preparedness attitudes
Difficult to Prepare P2 Change Home Damage P2 Change Life Disruption P2 Change
N. Carolina 36 -7 26 -1 46 -11
Washington 29 -6 16 0 52 -11
Hawaii 16 -5 33 5 69 2
Alaska 11 -3 27 1 81 2
California 38 -2 22 1 60 -5
Oregon 22 -1 20 2 76 5
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Selected tsunami preparedness behavior
Seek Information P2 Change Discussed Preparedness P2 Change Family Emergency Plan
California 9 -13 46 15 27
N. Carolina 8 -10 26 7 27
Alaska 13 -6 82 6 36
Hawaii 19 -3 62 1 57
Washington 16 -3 75 -4 60
Oregon 27 8 80 2 59
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Social Capital

• Social capital refers to social networks, the
  reciprocities that arise from them, and the value
  of these for achieving mutual goals
• The more social capital in a community
  exhibited as trust, fellowship, associations,
  connections, networks, social intercourse, good
  will, sympathy, and norms of reciprocity the
  healthier the community

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Social Capital

• Networks of social capital facilitate a flow of
  information providing a basis for action and
  assisting in individual and community goal
  attainment (Coleman 1988 Putnam 2000)
• Lack of social capital impedes flow of
  information in communities and, ultimately,
  hinders a communitys capacity to resist threats
  or collectively take advantage of opportunities
  (Putnam 2000)

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Social Capital Quality of Life

• Quality of life one aspect of social capital
  was generally seen as high in our sample
  communities
• Almost 9 out of 10 respondents believe their
  community is a great place to live
• About 8 out of 10 believe their community is a
  safe place to live

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Social Capital Trust

• We asked about trust in various groups, including
  local government, community leaders, local law
  enforcement
• Highest levels of trust were in law enforcement
  to protect and maintain order in the community
  (50-60)
• Lowest levels of trust were in local government
  (25-30)

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Social Capital Community Attachment

• Items included contributing to local causes,
  attending public meetings, volunteerism,
  participation in local activities, working with
  others to improve the community
• About 9 out of 10 respondents reported having
  contributed to local causes
• About 6 out of 10 indicated they have engaged in
  the other forms of community activities

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Why warnings?
Warning Response Model (Mileti and
colleagues) Protective Action Decision Model
(Lindell and colleagues)
Official
Informal
Natural / Environmental Cues
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Phase I. How likely an alert?
Likely Maybe Unlikely
3 2 1
N2,519
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Phase 2. Perceived ability to recognize
distinguish environmental cues
N 1,086
Distinguish earthquakes from man-made shaking 3.8 1.2
Earthquakes shaking last longer than man-made 3.4 1.3
Earthquakes shaking stronger than man-made 3.9 1.2
Location of shore at low tide 4.5 0.8
Familiar with seafloor at low tide 3.5 1.2
Shoreline recedes faster during tsunami 4.3 0.9
Shoreline will always recede before tsunami 3.5 1.2
Tsunami wave shapes could be complex 4.3 0.9
Tsunami waves can vary over short distances 3.9 0.9
Tsunami waves will always look different 2.9 1.2
1 strongly disagree to 5 strongly agree
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Conclusions community extremes

• North Carolina
• Poor knowledge of existence of hazard risk
• High trust in leadership
• No tsunami warning system in place
• Little knowledge of how to prepare or existence
  of plans

C
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Conclusions community extremes

• Washington
• High hazard awareness
• Tsunami warning system in place
• Acceptance of tangible risk
• Knowledge of warning system and evacuation plan
  BUT
• Low trust in
• Adequacy of routes
• Role of media and leaders

W
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Conclusions community extremes
A

• Alaska
• High hazard awareness
• Tsunami warning system in place
• Acceptance of tangible risk
• Knowledge of warning system evacuation plan
• High self efficacy

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Conclusions implications for warnings

• No unique formula is applicable to all
  communities
• Awareness knowledge are NOT sufficient to
  create preparedness. e.g. Washington
• Communities are in different stages of knowledge
  and awareness and have different needs
• Warning system should incorporate informal
  warnings environmental cues

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Challenges

• Instrument design (community diversity)
• Geographic spread of researchers
• Cross-disciplinary differences in culture

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Quo vadis

• Psychometric model