Probabilistic Tsunami Hazard Assessment (PTHA) for resilience assessment of a coastal community

Park, Hyoungsu; Cox, Daniel T.; Barbosa, André R.

doi:10.1007/s11069-018-3460-3

Cited by 21 publications

(12 citation statements)

References 48 publications

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“…For example, robust probabilistic tsunami hazard assessment (PTHA) requires tsunami run-up estimates for a large number of scenarios to allow for accurate quantification of the hazard and related uncertainty (Mori et al, 2018). However, due to the computational burden associated with physics-based numerical simulation, a logic-tree approach is typically employed: it limits the number of scenarios based on historical earthquake characteristics (e.g., magnitude, recurrence interval) used to evaluate uncertainty in tsunami hazard (Annaka et al, 2007;Park and Cox, 2016;Park et al, 2018). The issue with the logic-tree approach is that it relies on expert judgment, which is difficult to justify.…”

Section: Introductionmentioning

confidence: 99%

Rapid prediction of alongshore run-up distribution from near-field tsunamis

2020

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Rapid prediction of the spatial distribution of the run-up from nearfield tsunamis is critically important for tsunami hazard characterization. Even though significant advances have been made over the last decade, physicsbased numerical models are still computationally intensive. Here, we present a response surface methodology (RSM)-based model called the tsunami run-up response function (TRRF). Derived from a discrete set of tsunami simulations, TRRF can produce a rapid prediction of a near-field tsunami run-up distribution that takes into account the influence of variable local topographic and bathymetric characteristics in a given region. This new method reduces the number of simulations required to build an RSM model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Using the northern region of Puerto Rico as a case study, we investigated the performance (accuracy, computational time) of the TRRF. The results reveal that the TRRF achieves reliable prediction while reducing the prediction time by six orders of magnitude (computational time: < 1 second per earthquake).

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Section: Introductionmentioning

confidence: 99%

Rapid prediction of alongshore run-up distribution from near-field tsunamis

2020

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“…The questions posed in this element are regarding the management of coastal natural resources, protection and maintenance of habitats, ecosystems, and natural products, planning and implementation of management activities, as well as assessment and investment in resource management in coastal areas (Hidayah et al, 2019;Park et al, 2018) The problems studied in this element are regarding tsunami risk assessment, comprehensive coastal disaster risk assessment, community participation, and access to information on tsunami risk assessment results according to the conditions of coastal communities (Kim et al, 2017;Paramesti, 2011;Susilorini et al, 2021).…”

Section: Management Of Coastal Resourcesmentioning

confidence: 99%

Coastal Vulnerability Study on Potential Impact of Tsunami and Community Resilience in Pacitan Bay East Java

2022

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Collisions or harsh shifting of earth plates accompanied by an earthquake in the ocean would pose a potential tsunami. The coastal area in Pacitan Bay East Java faces directly to the Indian Ocean and is prone to tsunami disasters. This study aims to determine the vulnerability level of the area and the resilience of coastal communities against tsunamis. The geographic Information System (GIS) method was used in this study. This study applied weighted overlay calculation with four components: elevation, slope, and distance from the beach and the river to measure the vulnerability level. Moreover, Coastal Community Resilience (CCR) method was applied to measure the predictive response of the communities. The results indicated that most of the area in Pacitan Bay (79,70%) was categorised into high to very high vulnerable against tsunamis. The CCR results showed the low index structure design and post-disaster recovery elements.

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Section: Introductionmentioning

confidence: 99%

Rapid prediction of alongshore run-up distribution from near-field tsunamis

Lee¹,

Irish²,

Weiss³

2022

Preprint

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Rapid prediction of the spatial distribution of the run-up from near- field tsunamis is critically important for tsunami hazard characterization. Even though significant advances have been made over the last decade, physics- based numerical models are still computationally intensive. Here, we present a response surface methodology (RSM)-based model called the tsunami run-up response function (TRRF). Derived from a discrete set of tsunami simulations, TRRF can produce a rapid prediction of a near-field tsunami run-up distribution that takes into account the influence of variable local topographic and bathymetric characteristics in a given region. This new method reduces the number of simulations required to build an RSM model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Using the northern region of Puerto Rico as a case study, we investigated the performance (accuracy, computational time) of the TRRF. The results reveal that the TRRF achieves reliable prediction while reducing the prediction time by six orders of magnitude (computational time: < 1 second per earthquake).

show abstract

Probabilistic Tsunami Hazard Assessment (PTHA) for resilience assessment of a coastal community

Cited by 21 publications

References 48 publications

Rapid prediction of alongshore run-up distribution from near-field tsunamis

Rapid prediction of alongshore run-up distribution from near-field tsunamis

Coastal Vulnerability Study on Potential Impact of Tsunami and Community Resilience in Pacitan Bay East Java

Rapid prediction of alongshore run-up distribution from near-field tsunamis

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