Validating a Tsunami Vulnerability Assessment Model (the PTVA Model) Using Field Data from the 2004 Indian Ocean Tsunami

2006 ◽  
Vol 40 (1) ◽  
pp. 113-136 ◽  
Author(s):  
Dale Dominey-Howes ◽  
Maria Papathoma
Keyword(s):  
Indian Ocean ◽  
Vulnerability Assessment ◽  
Field Data ◽  
Assessment Model ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Tsunami Vulnerability Assessment

scholarly journals Numerical Simulations of the 2004 Indian Ocean Tsunami Deposits Thicknesses and Emplacements

2018 ◽  
Author(s):  
◽  
Musa Al'ala ◽  
Hermann M. Fritz ◽  
Mirza Fahmi ◽  
Teuku Mudi Hafli
Keyword(s):  
Sediment Transport ◽  
Indian Ocean ◽  
Numerical Simulations ◽  
Field Data ◽  
Numerical Models ◽  
Indian Ocean Tsunami ◽  
Tsunami Deposit ◽  
Field Observations ◽  
Tsunami Deposits ◽  
2004 Indian Ocean Tsunami

Abstract. After more than a decade of recurring tsunamis, identification of tsunami deposits, a part of hazard characterization, still remains a challenging task not fully understood. The lack of sufficient monitoring equipment and rare tsunami frequency are among the primary obstacles that limit our fundamental understanding of sediment transport mechanisms during a tsunami. The use of numerical simulations to study tsunami-induced sediment transport was rare in Indonesia until the 2004 Indian Ocean tsunami. This study aims to couple two hydrodynamic numerical models in order to reproduce tsunami-induced sediment deposits, i.e., their locations and thicknesses. Numerical simulations were performed using the Cornell Multi-Grid Coupled Tsunami Model (COMCOT) and Delft3D. This study reconstructed tsunami wave propagation from its source using COMCOT, which was later combined with Delft3D to map the location of the tsunami deposits and calculate their thicknesses. Two Dimensional-Horizontal (2DH) models were used as part of both simulation packages. Lhoong, in the Aceh Besar District, located approximately 60 km southwest of Banda Aceh, was selected as the study area. Field data collected in 2015 and 2016 validated the forward modeling techniques adopted in this study. However, agreements between numerical simulations and field observations were more robust using data collected in 2005, i.e., just months after the tsunami (Jaffe et al., 2006). We conducted pit (trench) tests at select locations to obtain tsunami deposit thickness and grain size distributions. The resulting numerical simulations are useful when estimating the locations and the thicknesses of the tsunami deposits. The agreement between the field data and the numerical simulations is reasonable despite a trend that overestimates the field observations.

scholarly journals Numerical simulations of the 2004 Indian Ocean tsunami deposits' thicknesses and emplacements

2019 ◽  
Vol 19 (6) ◽  
pp. 1265-1280 ◽  
Author(s):  
◽  
Musa Al'ala ◽  
Hermann M. Fritz ◽  
Mirza Fahmi ◽  
Teuku Mudi Hafli
Keyword(s):  
Sediment Transport ◽  
Indian Ocean ◽  
Numerical Simulations ◽  
Field Data ◽  
Numerical Models ◽  
Indian Ocean Tsunami ◽  
Tsunami Deposit ◽  
Field Observations ◽  
Tsunami Deposits ◽  
2004 Indian Ocean Tsunami

Abstract. After more than a decade of recurring tsunamis, identification of tsunami deposits, a part of hazard characterization, still remains a challenging task that is not fully understood. The lack of sufficient monitoring equipment and rare tsunami frequency are among the primary obstacles that limit our fundamental understanding of sediment transport mechanisms during a tsunami. The use of numerical simulations to study tsunami-induced sediment transport was rare in Indonesia until the 2004 Indian Ocean tsunami. This study aims to couple two hydrodynamic numerical models in order to reproduce tsunami-induced sediment deposits, i.e., their locations and thicknesses. Numerical simulations were performed using the Cornell Multi-grid Coupled Tsunami (COMCOT) model and Delft3D. This study reconstructed tsunami wave propagation from its source using COMCOT, which was later combined with Delft3D to map the location of the tsunami deposits and calculate their thicknesses. Two-dimensional horizontal (2-DH) models were used as part of both simulation packages. Four sediment transport formulae were used in the simulations, namely van Rijn 1993, Engelund–Hansen 1967, Meyer-Peter–Mueller (MPM) 1948, and Soulsby 1997. Lhoong, in the Aceh Besar District, located approximately 60 km southwest of Banda Aceh, was selected as the study area. Field data collected in 2015 and 2016 validated the forward modeling techniques adopted in this study. However, agreements between numerical simulations and field observations were more robust using data collected in 2005, i.e., just months after the tsunami (Jaffe et al., 2006). We conducted pit (trench) tests at select locations to obtain tsunami deposit thickness and grain size distributions. The resulting numerical simulations are useful when estimating the locations and the thicknesses of the tsunami deposits. The agreement between the field data and the numerical simulations is reasonable despite a trend that overestimates the field observations.

Andaman Seacoast of Thailand Field Survey after the December 2004 Indian Ocean Tsunami

2006 ◽  
Vol 22 (3_suppl) ◽  
pp. 187-202 ◽  
Author(s):  
Absornsuda Siripong
Keyword(s):  
Land Use ◽  
Indian Ocean ◽  
Satellite Imagery ◽  
Field Data ◽  
Field Survey ◽  
Andaman Sea ◽  
Indian Ocean Tsunami ◽  
Tide Gauges ◽  
2004 Indian Ocean Tsunami ◽  
Near Shore

The post-tsunami runups on the damaged Andaman Sea coastline of Thailand from the tsunami of 26 December 2004 were surveyed by Thai and Korean teams for 99 transects from 23 January to 7 February 2005. The highest runup in Thailand was 15.68 m at Cape Coral, in Phang-nga province, and the longest inundation distance was 3 km at Bang Nieng, in Phang-nga province. The causes of the variation in runup were analyzed by using the method of splitting tsunami (MOST) model, tide gauges, satellite imagery, and field data with topographic charts. The distribution of runups reflects the effects of bathymetry, coastal topography, coastline configuration and slope, the pattern and density of land use, and the biological and geomorphological characteristics of offshore and near-shore areas.

Medieval forewarning of the 2004 Indian Ocean tsunami in Thailand

Nature ◽  
2008 ◽  
Vol 455 (7217) ◽  
pp. 1228-1231 ◽  
Author(s):  
Kruawun Jankaew ◽  
Brian F. Atwater ◽  
Yuki Sawai ◽  
Montri Choowong ◽  
Thasinee Charoentitirat ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami

A snapshot of the 2004 Indian Ocean tsunami: societal impacts and consequences

2006 ◽  
Vol 15 (1) ◽  
pp. 163-177 ◽  
Author(s):  
Havidan Rodriguez ◽  
Tricia Wachtendorf ◽  
James Kendra ◽  
Joseph Trainor
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Societal Impacts

scholarly journals Developing tsunami fragility curves based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand

2011 ◽  
Vol 11 (1) ◽  
pp. 173-189 ◽  
Author(s):  
A. Suppasri ◽  
S. Koshimura ◽  
F. Imamura
Keyword(s):  
Indian Ocean ◽  
Building Materials ◽  
Fragility Curves ◽  
Distribution Functions ◽  
Least Square ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Inundation Depth ◽  
High Resolution Satellite Images ◽  
Tsunami Fragility

Abstract. The 2004 Indian Ocean tsunami damaged and destroyed numerous buildings and houses in Thailand. Estimation of tsunami impact to buildings from this event and evaluation of the potential risks are important but still in progress. The tsunami fragility curve is a function used to estimate the structural fragility against tsunami hazards. This study was undertaken to develop fragility curves using visual inspection of high-resolution satellite images (IKONOS) taken before and after tsunami events to classify whether the buildings were destroyed or not based on the remaining roof. Then, a tsunami inundation model is created to reconstruct the tsunami features such as inundation depth, current velocity, and hydrodynamic force of the event. It is assumed that the fragility curves are expressed as normal or lognormal distribution functions and the estimation of the median and log-standard deviation is performed using least square fitting. From the results, the developed fragility curves for different types of building materials (mixed type, reinforced concrete and wood) show consistent performance in damage probability and when compared to the existing curves for other locations.

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