Uncertainty in Tsunami Sediment Transport Modeling

Abstract

Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. We explore sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami, study site, available input data, sediment grain size, and model. Although uncertainty has the potential to be large, published case studies indicate that both forward and inverse tsunami sediment transport models perform well enough to be useful for deciphering tsunami characteristics, including size, from deposits. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and development of hybrid modeling approaches to exploit the strengths of forward and inverse models.

Inverse Tsunami Flow Modeling Including Nonequilibrium Sediment Transport, With Application to Deposits From the 2011 Tohoku‐Oki Tsunami

“…Finally, the evaluation of uncertainty in estimation is an essential problem in this research field as pointed out by Jaffe et al (). This study has revealed that the observed thickness and grain size distribution of the tsunami deposits can be reproduced by multiple sets of model input parameters.…”

“…There are two types of inverse models of the tsunami deposits: the point and 1‐D line models (Jaffe et al, ). Point models such as Moore et al (), Jaffe and Gelfenbaum (), or Naruse et al () consider flow properties from input data of the tsunami deposits at a single location, whereas the 1‐D line models such as Soulsby et al () or Tang and Weiss () reconstruct the behavior of the tsunami from data of the tsunami deposit collected at multiple sampling points on a transect normal to the shoreline.…”

“…There are two types of inverse models of the tsunami deposits: the point and 1-D line models (Jaffe et al, 2016). Point models such as Moore et al (2007), Jaffe and Gelfenbaum (2007), or Naruse et al (2012) consider flow Point models have an advantage in regard to ease to collect data from the deposit, but the models are strongly limited in their applications.…”

Modelling scour and deposition in harbours due to complex tsunami‐induced currents

“…Tsunami-induced nearshore hydrodynamics tend to be especially chaotic and unpredictable due to the existence of various types of diffusive sources [e.g. bottom boundary shear in Lynett et al (2012), wave breaking in Yoon and Cox (2010), active sedimentation in Cheng and Weiss (2013)], and significant uncertainty remains about sediment processes (Elfrink and Baldock, 2002;Jaffe et al, 2016). Shoreline evolution induced by tsunami waves can have a negative impact on both human structures and marine ecosystems; scouring near coastal structures (Tomita et al, 2006), sediment deposits in harbour basins and navigation channels, beach loss, and loss of habitat for marine species.…”

Could tsunami risk be underestimated using core‐based reconstructions? Lessons from ground penetrating radar

“…This provides important information for inverse/forward modelling which enables the reconstruction of the magnitude of past events (e.g. Jaffe and Gelfenbaum, 2007;Sugawara et al, 2014;Jaffe et al, 2011Jaffe et al, , 2016. However, because the thickness of tsunami sediments can vary significantly (Goto et al, 2011), it is essential to validate whether or not the deposit is a local feature, or if it represents a more ubiquitous event.…”