This thesis presents two conceptually linked papers outlining methodologies to improve community resilience. Both papers employ Seaside, Oregon as a testbed community and consider the seismic-tsunami hazard posed by the Cascadia Subduction Zone. The first paper presents a framework to deaggregate the results of a multi-hazard, multi-infrastructure, damage analysis by introducing six dimensions of deaggregation. The ensuing deaggregation shows how economic loss and risk plots can allow community resilience planners the ability to isolate high-risk events, as well as provide insights into the underlying driving forces. Geospatial representation of the results allows for the identification of both vulnerable buildings and areas within a community and is highlighted by the spatial pattern of parcel disconnection from critical facilities. The incorporation of population characteristics provides an understanding of how hazards disproportionately impact population subgroups and can aide in equitable resilience planning. The second paper presents a spatially explicit decision support framework using Bayesian networks to evaluate resilience at the parcel- and community-level. By applying this framework to the testbed community, it is first shown that under status quo conditions, the hazard associated with the 1,000-yr event results in overall low community resilience and that the seaward most parcels are less resilient compared to their inland counterparts. Second, deaggregated results demonstrate that this low resilience is driven by the electric and water networks’ time to recover. Third, by retrofitting and decreasing repair times associated with these infrastructures, the use of decision nodes within the Bayesian network are shown to increase inland parcel resilience. And fourth, resilience across various recurrence intervals shows that the community is least resilient against mid-magnitude events.
Funding Statement (additional comments about funding)
Funding for this study was provided as part of the cooperative agreement 70NANB15H044 between the National Institute of Standards and Technology (NIST) and Colorado State University through a subaward to Oregon State University. The content expressed in this paper are the views of the authors and do not necessarily represent the opinions or views of NIST or the U.S Department of Commerce.