TY - JOUR
T1 - Uncertainty in tsunami sediment transport modeling
AU - Jaffe, Bruce
AU - Goto, Kazuhisa
AU - Sugawara, Daisuke
AU - Gelfenbaum, Guy
AU - La Selle, Sean Paul
N1 - Publisher Copyright:
© 2016, Fuji Technology Press. All rights reserved.
PY - 2016/8
Y1 - 2016/8
N2 - 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 hybridmodeling approaches to exploit the strengths of forward and inverse models.
AB - 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 hybridmodeling approaches to exploit the strengths of forward and inverse models.
KW - Modeling
KW - Sedimentary deposits
KW - Tsunami sediment transport
KW - Uncertainty
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U2 - 10.20965/jdr.2016.p0647
DO - 10.20965/jdr.2016.p0647
M3 - Article
AN - SCOPUS:84981294583
SN - 1881-2473
VL - 11
SP - 647
EP - 661
JO - Journal of Disaster Research
JF - Journal of Disaster Research
IS - 4
ER -