Cited by 133Evacuation and Crowd DynamicsEarthquake and Tsunami EffectsEarthquake and Disaster Impact Studies

The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index; none of the 10 sources its claims cite is checked for support yet.

Anisotropic path modeling to assess pedestrian-evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest

Open access
Wood, Nathan J., Schmidtlein, Mathew C.·2012·Natural Hazards

Abstract

Recent disasters highlight the threat that tsunamis pose to coastal communities. When developing tsunami-education efforts and vertical-evacuation strategies, emergency managers need to understand how much time it could take for a coastal population to reach higher ground before tsunami waves arrive. To improve efforts to model pedestrian evacuations from tsunamis, we examine the sensitivity of least-cost-distance models to variations in modeling approaches, data resolutions, and travel-rate assumptions. We base our observations on the assumption that an anisotropic approach that uses path-distance algorithms and accounts for variations in land cover and directionality in slope is the most realistic of an actual evacuation landscape. We focus our efforts on the Long Beach Peninsula in Washington (USA), where a substantial residential and tourist population is threatened by near-field tsunamis related to a potential Cascadia subduction zone earthquake. Results indicate thousands of people are located in areas where evacuations to higher ground will be difficult before arrival of the first tsunami wave. Deviations from anisotropic modeling assumptions substantially influence the amount of time likely needed to reach higher ground. Across the entire study, changes in resolution of elevation data has a greater impact on calculated travel times than changes in land-cover resolution. In particular areas, land-cover resolution had a substantial impact when travel-inhibiting waterways were not reflected in small-scale data. Changes in travel-speed parameters had a substantial impact also, suggesting the importance of public-health campaigns as a tsunami risk-reduction strategy.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This study models pedestrian tsunami-evacuation times on Washington’s Long Beach Peninsula using least-cost-distance approaches and finds that an anisotropic path-distance model with high-resolution elevation and land-cover data best reflects evacuation conditions. It reports that thousands of residents, employees, and facilities would need ≥25 min to reach safety, and that modeling assumptions, elevation resolution, and travel speed strongly affect estimated exposure.

Key findings

  • Under the anisotropic model with 1-m elevation/land-cover data and a 1.1 m/s base speed, mean and maximum travel times to safety were 46 and 133 min; approximately 4,244 residents, 477 employees, 40 public venues, and 7 dependent-population facilities were at locations requiring ≥25 min to evacuate. Of the 4,244 residents, 1,204 were over 65 and 171 were under 5.
  • The distance-only model consistently underestimated travel times: average and maximum times were 30% and 24% lower than anisotropic results, and at the 25-min threshold it classified 13% fewer residents and 4% fewer employees as at risk. Differences reached up to 50 min, with the most frequent difference (32% of area) being 1–10 min. The roads model both underestimated and overestimated travel times by up to 40 min, while the isotropic model slightly underestimated them (4% lower average, 7% lower maximum).
  • Elevation resolution mattered more than land-cover resolution across the study area: using 10-m instead of 1-m elevation data lowered average and maximum evacuation times by about 60–63% and undercounted at-risk residents (66–68% of the 1-m estimate), employees (84–86%), public venues (80%), and dependent-population facilities (71%). Using 30-m NLCD land cover instead of 1-m land cover had minimal overall population-exposure impact (0–5% differences), though local water-body omissions caused underestimates.
  • Travel speed strongly affected exposure: at the 25-min threshold, the share of residents at risk ranged from 7% under a fast-run assumption (3.83 m/s) to 49% under a slow-walk assumption (0.91 m/s); employees ranged from 6% to 62%. Walking quickly (1.52 m/s) instead of slowly (0.91 m/s) could allow about 900 more residents (10% of the study area) to evacuate.

Methods

  • The study area was the Long Beach Peninsula, Washington, using a tsunami-hazard zone based on a Cascadia subduction zone Mw 9.1 scenario; the first tsunami wave was assumed to arrive about 25 min after the earthquake, and the safe zone was land outside the inundation zone.
  • Four least-cost-distance approaches were compared: distance-only, road-constrained, isotropic slope/land-cover, and anisotropic path-distance with slope directionality. Land-cover speed-conservation values were derived from Soule and Goldman energy-cost coefficients, and slope speed was based on Tobler’s hiking function; the base travel speed was 1.1 m/s.
  • Sensitivity tests varied elevation data (1-m LiDAR DEM vs 10-m NED proxy), land-cover data (1-m manual interpretation vs 30-m NLCD), and travel speeds (walking and running rates). Resident, employee, public-venue, and dependent-population datasets were overlaid on travel-time surfaces to estimate exposure at the 25-min threshold.

Limitations

  • Stated by the paper: the models simplify reality and cannot fully capture individual behavior and mobility, dynamic landscape conditions, or person–environment interactions; they assume immediate self-evacuation, constant travel speeds without fatigue, independent land-cover and slope effects, and no route capacity or congestion. The analysis also assumes uniform 25-min inundation across the study area, omitting inland wave-propagation delay.
  • Stated by the paper: land-cover speed values were based on energy-cost coefficients rather than empirical walking-speed data; 30-m NLCD data were disaggregated to 10-m cells; buildings and water were treated as impassable; and population layers were static point or facility representations.
  • Evident from the text: the road-constrained model can force unrealistic routes and may not represent actual pedestrian behavior, and the study does not validate modeled travel times against observed evacuations or field-measured walking speeds.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s11069-011-9994-2

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1 Maps of the a tsunami-hazard zone on the Long Beach Peninsula, Washington, b land cover of the study area based on manual interpretation of 1-m imagery, and c the study area relative to the State of Washington, United States of America (USA)
Fig. 1 Maps of the a tsunami-hazard zone on the Long Beach Peninsula, Washington, b land cover of the study area based on manual interpretation of 1-m imagery, and c the study area relative to the State of Washington, United States of America (USA)CC-BY-NC · From the PDF
Image 2
CC-BY-NC · From the PDF
Image 3
CC-BY-NC · From the PDF
Image 4
CC-BY-NC · From the PDF
Fig. 5 Maps showing pedestrian travel times across the Long Beach Peninsula, Washington, based on an anisotropic modeling approach and various data sets. a Shows results of the anisotropic using 1-m elevation data and 1-m land-cover data. b -d Show the difference at each grid cell between a and b 1-m elevation and 30-m NLCD data, c 10-m elevation and 1-m land-cover data, and d 10-m elevation and 30-m NLCD data
Fig. 5 Maps showing pedestrian travel times across the Long Beach Peninsula, Washington, based on an anisotropic modeling approach and various data sets. a Shows results of the anisotropic using 1-m elevation data and 1-m land-cover data. b -d Show the difference at each grid cell between a and b 1-m elevation and 30-m NLCD data, c 10-m elevation and 1-m land-cover data, and d 10-m elevation and 30-m NLCD dataCC-BY-NC · From the PDF
Fig. 6 Graphs showing influence of data resolution on a the cumulative percentage of grid cells relative to calculated travel times to safety, and b the percentage of grid cells relative to differences in travel times
Fig. 6 Graphs showing influence of data resolution on a the cumulative percentage of grid cells relative to calculated travel times to safety, and b the percentage of grid cells relative to differences in travel timesCC-BY-NC · From the PDF
Fig. 7 Maps showing the influence of land-cover data on evacuation-modeling outputs near the community of Oysterville (Fig. 1). Land-cover input data include a NLCD data derived from 30-m LandSat TM imagery and b 1-m data derived from manual interpretation of 1-m NAIP imagery. Model outputs (travel times in min) are based on an anisotropic approach using 1-m elevation data and either c 30-m NLCD data or d 1-m land-cover data
Fig. 7 Maps showing the influence of land-cover data on evacuation-modeling outputs near the community of Oysterville (Fig. 1). Land-cover input data include a NLCD data derived from 30-m LandSat TM imagery and b 1-m data derived from manual interpretation of 1-m NAIP imagery. Model outputs (travel times in min) are based on an anisotropic approach using 1-m elevation data and either c 30-m NLCD data or d 1-m land-cover dataCC-BY-NC · From the PDF
Fig. 8 Graphs showing influence of travel speeds on a the cumulative percentage of grid cells in the study area, b the cumulative percentage of residents ( n = 9,008) as a function of calculated travel times to safety, and c the cumulative percentage of employees ( n = 779) as a function of calculated travel times to safety
Fig. 8 Graphs showing influence of travel speeds on a the cumulative percentage of grid cells in the study area, b the cumulative percentage of residents ( n = 9,008) as a function of calculated travel times to safety, and c the cumulative percentage of employees ( n = 779) as a function of calculated travel times to safetyCC-BY-NC · From the PDF

Verification

Claims the paper makes

79 Own result · 35 Own interpretation · 21 No basis given · 9 Sources not found · 2 Own method · 1 No full text of the sources

Show all 92 claims

  • It may state two separate claims as one. 38
  • Probably not a substantial claim. 24
55 of 55
  • The authors state this as an assumption.

    Pedestrian fatigue is minimal and travel speeds remain constant during evacuation.

    “For the purposes of this analysis, we assume that pedestrian fatigue is minimal and travel speeds remain constant.”
  • The authors state this as an assumption.

    Many evacuees will be incapable of running because of age, health, or other mobility issues.

    “An approach proposed by Hayes ( described in Norman 2004) is empirically based but is based on wilderness runner data and we assume many evacuees will be incapable of running because of age, health, or other mobility issues (e.g., parents carrying children).”
  • The authors state this as an assumption.

    Walking speeds will decrease in proportion to the changes in energy required to move across different land-cover types.

    “This approach assumes walking speeds will decrease in proportion to the changes in energy required to move across different land-cover types.”
  • The authors state this as an assumption.

    An anisotropic, path-distance approach to LCD pedestrian-evacuation modeling is the most appropriate approach because it incorporates variations in land cover and the directionality of an evacuation.

    “We assume that an anisotropic, path-distance approach to LCD pedestrian-evacuation modeling is the most appropriate approach because it incorporates variations in land cover and the directionality of an evacuation.”
  • The authors state this as an assumption.

    All residents would be capable of moving at the quicker speed.

    “assuming all residents would be capable of moving at the quicker speed.”
  • The authors state this as an assumption.

    Tsunami arrival is 25 min after the initial earthquake.

    “assumed here to be 25 min after the initial earthquake”
  • The authors state this as an assumption.

    People at locations where travel time is 25 min or less could evacuate in time.

    “people we assume that could evacuate in time”
  • The authors state this as an assumption.

    The speed conservation values for slope and land cover are independent of one another.

    “Doing so, however, assumes that the SCV for slope and land cover are independent of one another (e.g., the impact of movement on sand versus roads will remain constant regardless of the slope on which they are found).”
  • The authors state this as an assumption.

    Evacuation speeds remain constant over long distances, ignoring the influence of fatigue.

    “As noted earlier, we assumed that evacuation speeds would remain constant over long distances, ignoring the influence of fatigue”
  • The authors state this as an assumption.

    Travel speeds over various land-cover types can be estimated based on Soule and Goldman's (1972) terrain energy coefficients.

    “and that travel speeds over various land-cover types could be estimated based on Soule and Goldman's (1972) terrain energy coefficients.”
  • The authors state this as an assumption.

    All locations in the study area will be inundated at the same time.

    “That assumes that all locations in the study area will be inundated at the same time; however, only the coastline would be inundated at 25 min after the initial earthquake.”
  • The authors state this as an assumption.

    People will initiate self-evacuations immediately after ground-shaking ends.

    “For instance, maps of travel times presented in this paper assume that people will initiate self-evacuations immediately after ground-shaking ends.”
  • The paper relies on this without support.

    Evacuations will be delayed as individuals take time to process natural cues, validate observations, fulfill caregiver roles, or gather items.

    “In reality, evacuations will be delayed as individuals take time to process observed natural cues, such as prolonged ground shaking or shoreline draw-down.”
  • The paper relies on this without support.

    Many people may stick to roads during an evacuation.

    “To account for the likelihood that many people may stick to roads during an evacuation, results of the anisotropic modeling approach could be combined with results of a roads-only modeling approach (for example, results represented as a range or with some sort of error bar).”
14 of 14

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Citations

Cites

Show all 18 references

Research works printed but not linked: 5

  1. Butler B, Cohen J, Putnam T, Bartlette R, Bradshaw L (2000) A method for evaluating the effectiveness of firefighter escape routes. In: Proceedings of the 2000 international wildfire safety summit, pp 42–53
  2. Cascadia Region Earthquake Workgroup (2005) Cascadia subduction zone earthquakes—a magnitude 9.0 earthquake scenario. Oregon Department of Geology and Mineral Industries, Portland
  3. Hewett M (2005) Automating feature extraction with the ArcGIS spatial analyst extension. 2005 ESRI international users conference proceedings. Available at http://proceedings.esri.com/library/userconf/proc05/papers/pap2109.pdf . Accessed 16 Aug 2010
  4. National Research Council (2011) Tsunami warning and preparedness: an assessment of the US tsunami program and the nation’s preparedness efforts, Committee on the Review of the Tsunami Warning and Forecast System and Overview of the Nation’s Tsunami Preparedness, National Research Council, 284 pp
  5. Scarf P (1998) An empirical basis for Naismith’s rule. Math Today Bull Inst Math Appl 34(5):149–152

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Metadata

Authors
Wood, N. J., & Schmidtlein, M. C.
Venue
Natural Hazards
Published
2011-10-16
Volume
62
Issue
2
Pages
275-300
ISSN
  • 0921-030X (ISSN-L)
  • 1573-0840
OpenAlex
W2068875301
Cited by
133
References
18
Citations by year
2026: 7 · 2025: 7 · 2024: 11 · 2023: 14 · 2022: 11 · 2021: 11 · 2020: 7 · 2019: 18 · 2018: 7 · 2017: 9 · 2016: 9 · 2015: 7 · 2014: 9 · 2013: 3 · 2012: 2
FWCI
19.40
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Natural Hazards (publishedVersion, cc-by-nc)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • U.S. Geological Survey
  • Oregon Sea Grant, Oregon State University
Topics
  • Social Sciences › Safety Research › Evacuation and Crowd Dynamics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Earthquake and Tsunami Effects
  • Social Sciences › Geography, Planning and Development › Earthquake and Disaster Impact Studies
Keywords
tsunami evacuation, crowd evacuation, vertical evacuation, Cascadia Subduction Zone earthquake, near-field tsunami, travel time, land cover, travel speed, tsunami risk reduction, tsunami education

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