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The Official Magazine of the Oceanography Society OceTHE OFFICIALa MAGAZINEnog OF THE OCEANOGRAPHYra SOCIETYphy CITATION Geist, E.L., and P.J. Lynett. 2014. Source processes for the probabilistic assessment of tsunami hazards. Oceanography 27(2):86–93, http://dx.doi.org/10.5670/oceanog.2014.43. DOI http://dx.doi.org/10.5670/oceanog.2014.43 COPYRIGHT This article has been published inOceanography , Volume 27, Number 2, a quarterly journal of The Oceanography Society. Copyright 2014 by The Oceanography Society. All rights reserved. USAGE Permission is granted to copy this article for use in teaching and research. Republication, systematic reproduction, or collective redistribution of any portion of this article by photocopy machine, reposting, or other means is permitted only with the approval of The Oceanography Society. Send all correspondence to: [email protected] or The Oceanography Society, PO Box 1931, Rockville, MD 20849-1931, USA. DOWNLOADED FROM HTTP://WWW.TOS.ORG/OCEANOGRAPHY SPECIAL ISSUE ON UNDERSEA NATURAL HAZARDS Source Processes for the Probabilistic Assessment of Tsunami Hazards BY ERIC L. GEIST AND PATRICK J. LYNETT ABSTRACT. The importance of tsunami hazard assessment has increased in recent years as a result of catastrophic consequences from events such as the 2004 Indian Ocean and 2011 Japan tsunamis. In particular, probabilistic tsunami hazard assessment (PTHA) methods have been emphasized to include all possible ways a tsunami could be generated. Owing to the scarcity of tsunami observations, a computational approach is used to define the hazard. This approach includes all relevant sources that may cause a tsunami to impact a site and all quantifiable uncertainty. Although only earthquakes were initially considered for PTHA, recent efforts have also attempted to Regional wavefield of tsunami from Currituck landslide at include landslide tsunami sources. Including these sources into PTHA is considerably three time steps. Evident in this more difficult because of a general lack of information on relating landslide area simulation is a secondary wave that refracts up the continental and volume to mean return period. The large variety of failure types and rheologies slope and toward shore north associated with submarine landslides translates to considerable uncertainty in of the Currituck landslide. determining the efficiency of tsunami generation. Resolution of these and several other LD wave: Leading depression wave. LE wave: Leading elevation wave. outstanding problems are described that will further advance PTHA methodologies From Geist et al. (2009) leading to a more accurate understanding of tsunami hazard. time = 7 minutes time = 16 minutes time = 55 minutes 300 250 LE wave 8 200 6 150 refracted 4 phase 100 2 y (km) 50 broadside 0 phase LD wave (m) Amplitude 0 –2 –50 –4 –100 –6 0 50 100 150 200 250 0 50 100 150 200 250 0 50 100 150 200 250 x (km) x (km) x (km) 86 Oceanography | Vol. 27, No. 2 INTRODUCTION The most common application is using deterministic analysis, probabilistic Recent events such as the 2004 Indian a pre-specified design probability analysis directly incorporates different Ocean and 2011 Tōhoku tsunamis, to determine the exceedance value sources of uncertainties into the among many others, have demonstrated at that probability (Figure 1a). For construction of the hazard curve. In the tremendous destructive nature of tsunami hazards, the hazard value of this article, we review the framework tsunamis as they impact coastlines. interest is typically runup (runup is a of PTHA that was established using Traditionally, tsunami hazards have measurement of the maximum height just earthquake sources and discuss been assessed deterministically using the of the water that the tsunami pushed recent advances in the methodology for concept of a maximum credible event or onshore observed above a reference including landslide sources. worst-case scenario. There is, however, sea level; see http://walrus.wr.usgs.gov/ no single accepted way of determining tsunami/basics.html), although other PTHA FRAMEWORK this scenario. In some cases, the physi- impact metrics such as current speed or PTHA was developed by adapting a cally largest earthquake or landslide is momentum flux can also be considered. long-standing probabilistic method for used to assess the tsunami hazard at a A less common application of the hazard determining ground motion exceedance coastal site, whereas in other approaches, curve is using a pre-specified tolerance caused by earthquakes: probabilistic the largest historical event is defined as level to determine the probability of seismic hazard analysis (PSHA) (Cornell, the worst-case scenario. Furthermore, exceedance from the hazard curve 1968). The main differences between the deterministic scenario is created (Figure 1b). For example, engineers PSHA and PTHA relate to which sources under an implicit assumption of a min- may be interested in the probability are considered and how the hazard is imum likelihood, though the likelihood that a sea wall of a certain height will calculated for earthquakes and tsunamis. limit is often not explicitly stated. For be overtopped by a tsunami. Finally, For PTHA, it is important to consider example, tsunami hazards from asteroid probabilistic inundation maps can also large tsunamigenic earthquakes, includ- impacts are rarely considered, owing to be constructed by calculating the hazard ing those at far distances, as well as other the long mean return period associated at a specific design probability over a tsunami sources such as submarine land- with this source. Finally, uncertainty given region (González et al., 2009), slides and volcanic processes that can associated with these scenarios is often useful for setting flood insurance rates rapidly displace the water column. For not rigorously defined or used in deter- (e.g., at 1% annual probability of exceed- PSHA, it is more important to consider ministic hazard assessments. To address ance). With information regarding vul- a wider range of earthquake magnitudes, these problems, tsunami hazards have nerability and exposure of a community, including small events, close to the site recently been assessed probabilistically. hazard curves permit a risk calculation. of interest. For the hazard calculation, While these assessments are resource Ideally, historical data from a nearby empirical attenuation relations are intensive compared to deterministic water level station could be used to used in PSHA that relate earthquake assessments, they provide a systematic construct a hazard curve. However, parameters to ground motion, whereas method for defining the hazard for a there are few locations in the world physics-based numerical hydrodynamic probability of interest specific to appli- where there is a sufficiently long record models are used in PTHA so that less cations such as tsunami preparedness, of tsunami observations. A computa- uncertainty can be expected, given determining flood insurance rates, and tional method, termed probabilistic sufficiently accurate input data. In this impact on infrastructure. tsunami hazard analysis (PTHA; Geist regard, PTHA shares some similarities In contrast to deterministic analysis and Parsons, 2006), is therefore required with probabilistic storm surge forecasts that estimates the severity of natural to construct the tsunami hazard curve. that also rely on numerical modeling hazard as a single value, the primary These methods require specifying all (e.g., Resio et al., 2009). result from probabilistic analysis is a relevant tsunami sources that may There are four basic steps in hazard curve that plots the probability affect the site, performing numerical conducting PTHA (Figure 2, middle): that a given severity will be met or hydrodynamic modeling for each (1) specification of source parameters, exceeded. The hazard curve can be source, and aggregating the results to including rate of occurrence, (2) choice used in two different ways (Figure 1). form the hazard curve. In contrast to of probability model that describes Oceanography | June 2014 87 source occurrence in time (most often owing to the natural variability of analysis (Parsons and Geist, 2009; Poisson, as described below), (3) hydro- tsunami evolution (aleatory uncertainty). Grezio et al., 2010). dynamic modeling for each source The latter is directly integrated into A perhaps subtle assumption in many location and set of source parameters to the hazard curve through a defined probabilistic analyses of tsunamis and compute tsunami hazard characteristics probability distribution that models the other natural hazards is that events are at a site, and (4) aggregation of the uncertainty. The former is incorporated random in time and unrelated to one modeling results and incorporation of through the use of logic trees (Figure 2, another, termed a Poisson process. The uncertainty. It is easy to see, with the bottom). Each branch of the logic tree time between tsunami events in such a number of sources involved and the is a separate calculated hazard curve process follows an exponential distri- many possible combinations of source that is weighted according to a specific bution. Accordingly, the hazard curve parameters, that the computational subjective (i.e., expert consensus) or represents the probability that one or load associated with PTHA can become objective (i.e., normal distribution) more tsunamis will meet or exceed the a major obstacle in performing the scheme. The final hazard curve is corresponding runup on the horizontal analysis. There are several
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