Research Question

The Pacific Northwest is subject to the rupture of the Cascadia Subduction Zone (CSZ) which will result in an earthquake and nearfield tsunami. Low-lying communities along the coast (such as Seaside, OR) are susceptible to infrastructure damage from both earthquake ground shaking and tsunami inundation. Given resource constraints (budget, personnel, time, etc.), it is not feasible for city planners and resource managers to expect to mitigate all possible damages resulting from an earthquake/tsunami event; however, it is possible to optimize resources in order to minimize the expected damages. Consequently, a first step toward resource optimization is a thorough understanding of the risks posed by the CSZ.

For this project, I investigated how the spatial pattern of tax-lot damage and connectivity to critical infrastructure following a multi-hazard earthquake/tsunami in Seaside, OR can provide insight into possible hazard mitigation measures. The damages and connectivity were deaggregated by both hazard (earthquake vs. tsunami) as well as intensity of the event. An understanding of the deaggregated hazard provides insight into vulnerable areas within Seaside.

Description of Dataset

My dataset consists of maps of the built environment (or infrastructure) and hazard maps (earthquake ground shaking and tsunami inundation) for Seaside, OR. The infrastructure is composed of buildings, an electric power network, a transportation network, and a water supply network (Figure 1). The earthquake and tsunami hazard maps were previously generated through numerical modeling (Park, Cox, Alam, & Barbosa, 2017). Due to computational limitations, tsunami inundation is modeled using a “bare-earth” model indicating that no infrastructure is included.

In addition to the infrastructure and hazard maps, a suite of probabilistic damage codes were utilized. These damage codes implement Monte-Carlo methods to evaluate the expected damages, economic losses, and connectivity associated with earthquake/tsunami hazards.

 

Hypotheses

The project was divided into three exercises. The questions posed by each exercise, and hypotheses are outlined below:

1. What is the spatial pattern of economic losses resulting from a joint earthquake/tsunami event? How does each hazard contribute to this spatial pattern?
   1. Earthquakes and tsunamis pose different hazards to the built environment. The former results in strong ground shaking whereas the latter results in inundation. Tsunami inundation is much more spatially variable due to limiting characteristics such as ground elevation and friction losses. Conversely, earthquake ground shaking is not limited by elevation or friction and is subsequently not as spatially variable within a geographic region (especially at scales the size of Seaside). Because of these differences in hazardous conditions, I expect the spatial pattern of tsunami losses to be concentrated along the coast, whereas the spatial pattern of economic losses will be dispersed around Seaside.

 

2. How does the spatial pattern of economic losses relate to the spatial pattern of tsunami momentum flux?
   1. Because there is significantly more spatial variability in the tsunami hazard compared to the earthquake hazard, I wanted to investigate how this spatial pattern relates to the spatial pattern of tsunami economic losses. Economic losses are driven by the intensity of the hazard, therefore, I expect a significant correlation between economic losses and tsunami momentum flux (a measure of both inundation depth and velocity).

 

3. How vulnerable is Seaside’s networked infrastructure to a joint earthquake/tsunami event?
   1. While economic losses to infrastructure can play a role in hazard mitigation planning, it should not serve as the only driver. Mitigation planning should also (and perhaps more importantly) consider resident accessibility to critical services and facilities immediately following a disaster. Given the importance of resident accessibility, I wanted to perform a connectivity analysis of networked infrastructure (electricity, transportation, and water) following a joint earthquake/tsunami event. The networked infrastructure in Figure 1 shows that failure of a few key links could severely limit large populations of Seaside (g. the network is not highly “parallel”, but exhibits some “series” features). For example, failure of the pipes leading to the water treatment plant (Figure 1-d) would result in complete disconnectivity of water to Seaside. Given the structure of the networks at Seaside, I expect sharp increases in the disconnectivity if some of the key links fail.

Approaches/Methods

1. Spatial pattern of economic losses:
   1. To evaluate the spatial pattern of economic losses, I created real market value and economic loss heatmaps. Economic loss heatmaps provide insight into densely populated regions within Seaside that could potentially benefit from a single mitigation option. Heatmaps were generated by using the kernel density estimation tool within the QGIS processing toolbox.
2. Relationship between economic losses and tsunami momentum flux:
   1. Performing a geographically weighted regression (GWR) provided insight into the relationship between economic losses and tsunami momentum flux. Here, the percent of economic loss depended on the tsunami momentum flux. The GWR was performed using the python spatial analysis library PySal.
3. Connectivity of networked infrastructure:
   1. Connectivity analyses between tax lots and critical infrastructure provided insight into the probability of tax lots becoming disconnected from critical infrastructure. Additionally, maps showing the probability of link failure were generated to isolate vulnerable links within the networks. The connectivity analysis was performed using the python network analysis package python-igraph.

Results

Spatial pattern of economic losses

Heatmaps showing the spatial pattern of economic losses deaggregated by both hazard and intensity are shown in Figure 2. It can be seen that a hot spot of damages is located at the central business district (CBD), and appears for low magnitude earthquake events. The tsunami damages are more evenly distributed along the coast, relative to the earthquake damages. Figure 3 shows the total economic risks for of Seaside deaggregated by hazard and intensity. Here, risk is defined as the economic losses multiplied by the annual probability of occurrence (the inverse of the return year). Quantifying the economic losses by risk allows for the isolation of events that are both likely to occur and produce significant damages. It can be seen in Figure 3 that the 250 to 1000-year events pose the highest economic risks. If economic losses are a priority, using Figures 2 and 3, a city planner could identify regions of buildings within Seaside that are vulnerable to earthquake damage. Subsequently these regions would benefit the most from mitigation options.

Relationship between economic losses and tsunami momentum flux

The relationship between percent of economic losses and tsunami momentum flux was measured by performing a geographically weighted regression (GWR). A key parameter of the GWR is the bandwidth, which describes the area under which the regression is performed. A bandwidth of 200 was initially used (see to exercise 2). The bandwidth has been further optimized by comparing the resulting r² values of multiple GWRs (Figure 4).  It can be seen that a bandwidth of 75 results in the largest r² value, and was subsequently used for further analysis. Bandwidths below this value resulted in errors in the GWR code.

The results from the GWR with an updated bandwidth are shown in Figure 5. It can be seen that the slope is small near to the coast with hotspots of larger values located further inland. Conversely, the intercept is large near to the coast, and decreases as further inland. The intercept can be explained by the large momentum flux near to the coast and decreases as the tsunami propagates landward.

Interestingly, some of the slope values are less than 0 indicating that the damages decrease as momentum flux increases. This is likely explained by the heterogeneity of building types within Seaside. For example, concrete buildings are more resistant to tsunami damage than wood buildings. Consequently, a concrete building that experiences a large momentum flux may result in less damage than a wood building that experiences a small momentum flux. To validate this, the buildings in Seaside were deaggregated by their characteristics and a linear regression was performed (Figure 6). The buildings can be classified according to the construction material (wood vs. concrete), year built, and number of floors. Here, W1 corresponds to one-story wood building; W2 corresponds to two-story wood building; and C1 corresponds to concrete buildings with moment a frame. The concrete moment frame can be further divided into less than or greater than 4 stories. It can be seen in Figure 6 that all regression slopes are positive with relatively high r² values. Considering the most extreme case, it can be seen that if a W1 building built before 1979 is next to a C1 building built after 2003, the resulting regression slope would likely be negative.

Connectivity of networked infrastructure

The probability of each tax lot being connected to critical facilities was evaluated by performing a connectivity analysis using the EPN, transportation, and water networks (see networks in Figure 1). The total fraction of tax lots disconnected from critical infrastructure are shown in Figure 7 (left-hand side). Similar to economic risks, the fraction of disconnection was multiplied by the probability of occurrence to determine the disconnectivity risk. It can be seen that for the 1000-year event, nearly all of Seaside becomes disconnected. Furthermore, the 250- to 500-year events pose the highest risk across the three networked infrastructures.

The network performance can also be characterized spatially by creating maps indicating the probability of link failure and tax lot disconnection. An example is shown in Figure 8 for the tsunami damage resulting from a 500-year event. This case was selected to view spatially because it drives the risk associated with the transportation network. Figure 8 shows that tax lots west of the Necanicum River have a high probability of becoming disconnected. Viewing the link failure map, it becomes clear that the disconnection is not driven by bridge failure, but rather from road washout.

Significance

The ability to spatially isolate vulnerable infrastructure within a community serves as a first step in optimizing resource management. Being able to identify highly vulnerable areas, ensures that resources are not spent in areas that are already relatively resilient to the earthquake/tsunami hazard. The risk results from this analysis show that Seaside is particularly vulnerable to the 250- to 1000-year rupture of the CSZ. There is a large concentration of economic value located at the city center of seaside. If resource managers and city planners place an emphasis on reducing economic losses, then they should focus on mitigation measures to reduce damages within this region. However, more important than economic losses is resident and tourist accessibility to critical facilities following a natural disaster. Network mitigation measures should focus on reducing the damages associated with the 250- to 500-year events.

The results from this analysis are not comprehensive and future work should include:

1. Spatial analysis of mitigation measures – Additional damage modeling should be performed with various mitigation measures in place. For example, the effect on transportation connectivity resulting from earthquake retrofitting of bridges can be incorporated into the damage model. Similar spatial maps as those produced in this project could be created in order to determine the spatial effect that mitigation measures have.
2. Incorporate population in analyses – Climate change and natural disasters tend to have a disproportionate effect on populations within a geographic region. Future work should include spatially mapping socio-economic vulnerability indices to identify vulnerable populations within Seaside.
3. Perform additional GWRs – It was shown that the building classification lead to counterintuitive results in the GWR. Additional GWRs could be performed by only considering similar building types with a geographic region.

Learning

Throughout this term, I learned how to both use new software as well as apply new statistical methods.

1. Software –
   1. Python – Prior to this course, I was familiar with both QGIS and python; however, I had not used the two together. A personal goal was to learn how to perform geospatial analysis in python. This was accomplished by performing the GWR using the python library I additionally learned how to read and modify shapefiles in python using the geospatial library geopandas.
   2. QGIS processing toolbox – I learned how to use multiple tools within the QGIS processing toolbox (g. heatmap interpolation and hotspot analysis).
2. Statistics –
   1. Hot spot analysis – Although I did not use hotspot analysis for my project, I learned about this method and did a test case for exercise 1. The hotspot analysis was used to isolate areas within Seaside that resulted in disproportionate damages relative to the surrounding area.
   2. Geographically weighted regression – Geographically weighted regression was performed to evaluate the relationship between tsunami momentum flux and percent damage.

As a significant portion of my research will be spatially-oriented, the tools and skills I learned during this term will be beneficial for future work. Furthermore, this course introduced additional geo-spatial statistic methods that were not implemented for this project but could be relevant for additional work.

References

Kameshwar, S., Farokhnia, K., Park, H., Alam, M. S., Cox, D., Barbosa, A., & van de Lindt, J. (n.d.). Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdepencies, and target objectives in a Bayesian network. Reliability Engineering and System Safety.

Park, H., Cox, D. T., Alam, M. S., & Barbosa, A. R. (2017). Probabilistic Seismic and Tsunami Hazard Analysis Conditioned on a Megathrust Rupture of the Cascadia Subduction Zone. Frontiers in Built Environment, 3(June), 1–19. https://doi.org/10.3389/fbuil.2017.00032