Numerical modeling of tsunami inundation using upscaled urban roughness parameterization

Muhari³

et al. 2020

Preprint

Following the eruption of Anak Krakatoa, aconsiderable landslide occurred on the southwestern part of the volcano and, upon entering the sea, generated a large tsunami within the Sunda Strait, Indonesia, on 22 December 2018. This tsunami traveled ~5 km across the strait basin and inundated the shorelines of Sumatra and Java with a vertical runupreaching 13 m. Following the event, observed field data, GPS measurements of the inundation, and multibeam echo soundings of the bathymetry within the strait were collected and publicly provided. Using this dataset, numerical modeling of the tsunami was conducted using the two-layer (soil and water) TUNAMI-N2 model based on a combination of landslidesources and bathymetry data. The two-layer model was implemented to nest the grid system using a finest grid size of 20 m. To constrain the unknown landslide parameters, the differential evolution (DE) global optimization algorithm was applied, which resulted in a parameter set that minimized the deviation from the measured bathymetry after the event. The DE global optimization procedure was effective at determining the landslide parameters for the model with the minimum deviation from the measured seafloor. The lowest deviation from the measured bathymetry was obtained for the best-fitting parameters:a maximum landslide thickness of 301.2 m and a landslide time of 10.8 minutes. The landslide volume of 0.182 km3 estimated by the best-fitting parameters shows that the tsunami flow depth could have reached 3-10 m along the shore with a K value of0.89, although thesimulated flow depths were underestimatedin comparison with the observation data. According to the waveforms, the general wave pattern was well reproduced at tide gauges during the event. A large number of objective function evaluations were necessary to locate the minimum with the DE procedure to fix the grid cell size to 20 m;this limited the accuracy of the obtained parameter values for the two-layer model. Moreover, considering the generalizations in the modeling of landslide movements, the impact landslide time and thickness must be carefully calculated to obtain a suitable accuracy.

mentioning

confidence: 99%

Global Optimization of a Numerical Two-layer Model Using Observed Data: A Case Study of the 2018 Sunda Strait Tsunami

Muhari³

et al. 2020

Preprint

“…The K and κ values do not agree with the JSCE standard because the governing equation of tsunami runup ignores the effect of a building inside the model, although the building obstructs the flow on the terrain. The obstruction of a building can increase the flow resistance relative to a model without the building, and therefore, the simulation of flow through a building can raise the water level close to the real situation (Copeland 2000;Dutta et al 2007;Aburaya and Imamura 2002;Fukui et al 2019). The inclusion of buildings within the tsunami runup modeling process constitutes the limitation of this study; hence, we suggest inputting buildings into the model in future work, which might provide flow depths close to the observation data.…”

Section: Discussionmentioning

confidence: 99%

Global optimization of a numerical two-layer model using observed data: a case study of the 2018 Sunda Strait tsunami

Muhari³

et al. 2020

Geosci. Lett.

Following the eruption of Mount Anak Krakatau, a considerable landslide occurred on the southwestern part of the volcano and, upon entering the sea, generated a large tsunami within the Sunda Strait, Indonesia, on December 22, 2018. This tsunami traveled ~ 5 km across the strait basin and inundated the shorelines of Sumatra and Java with a vertical runup reaching 13 m. Following the event, observed field data, GPS measurements of the inundation, and multibeam echo soundings of the bathymetry within the strait were collected and publicly provided. Using this dataset, numerical modeling of the tsunami was conducted using the two-layer (soil and water) TUNAMI-N2 model based on a combination of landslide sources and bathymetry data. The two-layer model was implemented to nest the grid system using the finest grid size of 20 m. To constrain the unknown landslide parameters, the differential evolution (DE) global optimization algorithm was applied, which resulted in a parameter set that minimized the deviation from the measured bathymetry after the event. The DE global optimization procedure was effective at determining the landslide parameters for the model with the minimum deviation from the measured seafloor. The lowest deviation from the measured bathymetry was obtained for the best-fitting parameters: a maximum landslide thickness of 301.2 m and a landslide time of 10.8 min. The landslide volume of 0.182 km3 estimated by the best-fitting parameters shows that the tsunami flow depth could have reached 3–10 m along the shore with a K value of 0.89, although the simulated flow depths were underestimated in comparison with the observation data. According to the waveforms, the general wave pattern was well reproduced at tide gauges during the event. A large number of objective function evaluations were necessary to locate the minimum with the DE procedure to fix the grid cell size to 20 m; this limited the accuracy of the obtained parameter values for the two-layer model. Moreover, considering the generalizations in the modeling of landslide movements, the impact landslide time and thickness must be carefully calculated to obtain a suitable accuracy.

“…The and values do not agree with the JSCE standard because the governing equation of tsunami runup ignores the effect of a building inside the model, although the building obstructs the flow on the terrain. The obstruction of a building can increase the flow resistance relative to a model without the building, and therefore, the simulation of flow through a building can raise the water level close to the real situation (Copeland 2000;Dutta et al 2007;Aburaya and Imamura 2002;Fukui et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Global Optimization Of A Numerical Two-Layer Model Using Observed Data: A Case Study Of The 2018 Sunda Strait Tsunami

Muhari³

et al. 2020

Preprint

Following the eruption of Mount Anak Krakatau, a considerable landslide occurred on the southwestern part of the volcano and, upon entering the sea, generated a large tsunami within the Sunda Strait, Indonesia, on 22 December 2018. This tsunami traveled ~5 km across the strait basin and inundated the shorelines of Sumatra and Java with a vertical runup reaching 13 m. Following the event, observed field data, GPS measurements of the inundation, and multibeam echo soundings of the bathymetry within the strait were collected and publicly provided. Using this dataset, numerical modeling of the tsunami was conducted using the two-layer (soil and water) TUNAMI-N2 model based on a combination of landslide sources and bathymetry data. The two-layer model was implemented to nest the grid system using a finest grid size of 20 m. To constrain the unknown landslide parameters, the differential evolution (DE) global optimization algorithm was applied, which resulted in a parameter set that minimized the deviation from the measured bathymetry after the event. The DE global optimization procedure was effective at determining the landslide parameters for the model with the minimum deviation from the measured seafloor. The lowest deviation from the measured bathymetry was obtained for the best-fitting parameters: a maximum landslide thickness of 301.2 m and a landslide time of 10.8 minutes. The landslide volume of 0.182 km3 estimated by the best-fitting parameters shows that the tsunami flow depth could have reached 3-10 m along the shore with a K value of 0.89, although the simulated flow depths were underestimated in comparison with the observation data. According to the waveforms, the general wave pattern was well reproduced at tide gauges during the event. A large number of objective function evaluations were necessary to locate the minimum with the DE procedure to fix the grid cell size to 20 m; this limited the accuracy of the obtained parameter values for the two-layer model. Moreover, considering the generalizations in the modeling of landslide movements, the impact landslide time and thickness must be carefully calculated to obtain a suitable accuracy.