Tsunami Awareness

Tsunami Awareness GIS Project¶

A couple of months ago, I was facing the decision of choosing a topic for my GIS capstone project — the most nerve-wracking part of the entire GIS certificate program. This final course was meant to showcase all of the skills learned over five quarters (15 months). I had the liberty to choose my topic, which was a challenge on its own.

Project Requirements¶

The minimum requirements for this GIS capstone project included:

1. Digitize or edit a study area using the Editor tool
2. Perform at least 4 geoprocessing tasks (e.g., union, intersect, clip) or raster algebra calculations
3. Geocode an attribute database or create points from XY coordinates
4. Run a minimum of 3 attribute queries, including a relational join
5. Update or calculate values in an attribute field
6. Create thematic map(s) with standard cartographic elements
7. Use ArcToolbox and ModelBuilder to automate your geoprocessing model

Why Tsunami?¶

As an open-water swimmer for over a year, I’ve come to respect the energy in ocean waves. One way to avoid a crashing wave is by duck diving under it. However, waves on land — like tsunamis — are a different story.

On September 30, 2022, NBC 6 (Miami) aired shocking footage of a rogue wave sweeping pedestrians into the ocean. Watching that moment made me realize how little I knew about tsunami safety, even though I had lived in coastal communities for years.

Giant Wave Sweeps People Off Sidewalk

This became the inspiration for my GIS capstone — studying tsunami risk in San Diego County, and how we can improve public awareness.

San Diego County Tsunami Risk¶

The 2023 Multi-Jurisdictional Hazard Mitigation Plan ranked tsunami risk across four categories:

• Geographic Area Affected: Negligible (<10% of county)
• Maximum Probable Extent: Weak
• Probability of Future Events: Unlikely (<1% annually)
• Overall Significance: Medium

While the affected area is limited, coastal zones are densely populated with critical infrastructure.

Local offshore faults — Coronado Bank, San Diego Trough, San Clemente–San Isidro — present unique risks due to their proximity. Response time from local events could be under 5 minutes.

Public Awareness Survey¶

Between May 7–18, 2023, I conducted a survey to assess public awareness:

• 38% believed a tsunami is “somewhat likely”
• 50% understood the difference between near-shore and far-field sources
• 46% were unaware that evacuation time from near-shore events could be under 5 minutes

Conceptual Model: Shelter Suitability¶

To support awareness and planning, I created a suitability model to identify evacuation shelters.

Inputs included:

• Inundation area
• Elevation (DEM)
• Hospitals, schools, fire stations
• Roads

Model outputs were classified into three classes and rasterized at ~203m resolution.

Inundation Analysis (2022 CGS Map)¶

Threatened Areas:

• San Diego: Harbor Island, Shelter Island, Ocean Beach, Mission Beach
• Coronado: Coronado City, Silver Strand
• Imperial Beach: Downtown IB
• Oceanside: Coastal properties

Threatened Structures¶

To identify structures at risk:

• Used Assessor parcel data
• Clipped with CGS inundation zones
• Joined attributes to assess land use, value, construction quality, living area, etc.

DEM + Parcel Heights¶

Used 2014 USGS QL2 LiDAR clipped to tsunami zones:

• Point spacing: 4
• Spatial resolution: 0.7 meters
• Elevation range: –10m to 507m

Joined to parcel polygons to assess:

• Maximum elevation
• Potential horizontal evacuation (30m+ height)
• Building resilience in densely populated, road-limited coastal areas

Schools & Roads¶

To support evacuation planning:

• Clipped schools with a 1-mile buffer
• Included highways, major roads, freeways

Clicking on a school or parcel reveals its attributes.

Shelter Selection¶

Final shelter sites were selected manually based on:

• Elevation
• Distance from inundation zones
• Access via major roads

Resources¶

• 📄 Full Report (Google Drive)
• 📊 Survey & Dataset (Google Sheets)

Next Steps¶

You can improve the shelter suitability model by adjusting weights for:

• Proximity to critical services
• Traffic capacity
• Land ownership

You can also build this as an interactive map using Leaflet or ipyleaflet inside Jupyter Book.