Preparing for Earthquakes in Dallas-Fort Worth Applying HAZUS to assess shelter accessibility. Crystal Curtis

Transcription

1 Preparing for Earthquakes in Dallas-Fort Worth Applying HAZUS to assess shelter accessibility Crystal Curtis

2 Introduction 20+ earthquakes in the city of Irving October 2014 to March 2015 HAZUS can be used to simulate effects of disasters What are the effects on the road network? How many people will need shelter? Can ArcMap adequately model shelter accessibility? Goal To encourage disaster planning in light of recent earthquakes by demonstrating how shelters can be identified using a road network that incorporates damaged bridges. SECTION TITLE 2

3 Background

4 Study area in Texas Balcones Fault Zone Source: Univ of Texas, ttex/centtex_where.html Barnett Shale Source: USGS

5 HAZUS Flood and Hurricane most used in Texas Ploeger et al used HAZUS on an area it hadn t been used before: Ottawa, Canada Neighbors et al compared multiple scenarios in Washington state Default vs user inventory Physical Earth Science Hazard model crucial Location-allocation Kongsomsaksakul and Wu both used mathematical approaches Weighted values on shelter needs

6 Data

7 Census Tracts Population and density Natural Jenks classification Inventory tied to census tract

8 Inventory data Some aggregated per tract, others listed individually Aggregated: residential and commercial buildings Individual: schools and bridges Table 1: A Sample of Individual Structures in a HAZUS Essential Facilities Category ID Number Class Tract Name TX EFHL Baylor Medical CTR at Irving TX EFHM Dallas Southwest Medical Center TX EFHM Baylor/Richardson Med Center TX EFHL Doctors Hospital of Dallas TX EFHS Mental Health Connections TX EFHL Parkland Health & Hospital

9 Bridges Created from the highway section of the transportation systems sub menu Multiple damage categories

10 Shelter Needs Can visualize per tract or get a text report Visualization allows identification of cluster problem areas

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12 Digitized fault lines Post 2009 earthquakes

13 Roads Highways 70 MPH Roads 40 MPH Shelters Non schools shelters geocoded using address Not individually identifiable in HAZUS Average capacity set to 250 Shelter damage not significant

14 Methodology

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16 Creating scenario Arbitrary Set properties Run for each fault Faults imported through epicenter definition map Extracting data from HAZUS Map shelter needs and bridge damage Export to ArcGIS Damaged bridges extracted from total

17 Creating the scenario 5.5 magnitude Epicenter along imported fault line Depth determined by average depth of past earthquakes in the area CEUS had no fault type selectable Ran for Azle and Irving

18 Creating network Damaged bridges as barrier Time based Location-allocation Centroids as starting points Shelter capacity of 250 Include other counties shelters

19 Results

20 Bridge damage In Dallas County Bridge damage in Tarrant County

21 Shelter needs in Dallas County Shelter needs in Tarrant County

22 Allocation for Tarrant County Some unallocated Remote area Possible influence of highways on shelter selection 30 MPH faster than roads Under ideal conditions

23 Allocation for Dallas County Many unallocated Possible influence of highways

24 Unallocated shelters in Dallas County Many with moderate needs

25 Distance based analysis Fewer outside the county None far away compared to time based 36 highways a large factor?

26 Conclusion

27 Conclusion Implication Irving fault may have larger consequences than Azle Unallocated points need solutions Build more shelters? Fix bridges? Limitations Models only approximate reality Default data in HAZUS wont be as accurate as user defined North Texas is mostly clay soils, so impact may be worse than shown Location-allocation does not provide routes

28 Conclusion Future Research Can apply to Oklahoma City Can identify problematic bridges for earthquakes