Operational tsunami modelling with TsunAWI – recent developments and applications

Rakowsky, Natalja; Androsov, Alexey; Fuchs, Annika; Harig, Sven; Immerz, Antonia; Danilov, Sergey; Hiller, Wolfgang; Schröter, Jens

doi:10.5194/nhess-13-1629-2013

Cited by 35 publications

(30 citation statements)

References 30 publications

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“…This arrival time definition is also used by the German-Indonesian Tsunami Early Warning System (GITEWS, Rakowsky et al 2013), and for the present implementation a threshold of l 1 ¼ 0:05 m is defined. However, considering that in some cases the tsunami time series might not exceed this prescribed threshold, a second arrival time is defined as the time when a proxy for the slope of the free surface exceeds the threshold l 2…”

Section: Performance Assessmentmentioning

confidence: 99%

A Multiple-Parameter Methodology for Placement of Tsunami Sensor Networks

Meza

Catalán

Tsushima

2019

Pure Appl. Geophys.

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A methodology to optimize the design of an offshore tsunami network array is presented, allowing determination of the placement of sensors to be used in a tsunami early warning system framework. The method improves on previous sensor location methods by integrating three commonly used tsunami forecast performance indicators as a measure of the predictive accuracy through a single cost function. The joint use of different tsunami parameters allows for a network that is less subject to bias found when using a single parameter. The resulting network performance was tested using a set of synthetic target scenarios and also verified against a model of the 2014 Pisagua event, suggesting that having such a network in place could have provided meaningful information for the hazard assessment. The small number of sensors required (three spanning nearly 700 km of the Northern Chile coast) may be useful in implementing such networks in places where funding of denser arrays is difficult.

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Section: Performance Assessmentmentioning

confidence: 99%

A Multiple-Parameter Methodology for Placement of Tsunami Sensor Networks

Meza

Catalán

Tsushima

2019

Pure Appl. Geophys.

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“…In the case of tsunami simulation, 2D non-linear shallow water equations are commonly implemented and solved numerically on a mesh or grid, and there are many software solutions available for tsunami simulation, such as TUNAMI [35], ANUGA, and TsunAWI [36]. Tsunami inundation is simulated through numerical solutions of the non-linear shallow water equations over a model of bathymetry and topography with appropriate extensions to model wetting and drying processes.…”

Section: Tsunami Simulationmentioning

confidence: 99%

Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation

Vinayaraj

Raghavan

Metz

et al. 2017

IJGI

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Accurate and high resolution bathymetric data is a necessity for a wide range of coastal oceanographic research topics. Active sensing methods, such as ship-based soundings and Light Detection and Ranging (LiDAR), are expensive and time consuming solutions. Therefore, the significance of Satellite-Derived Bathymetry (SDB) has increased in the last ten years due to the availability of multi-constellation, multi-temporal, and multi-resolution remote sensing data as Open Data. Effective SDB algorithms have been proposed by many authors, but there is no ready-to-use software module available in the Geographical Information System (GIS) environment as yet. Hence, this study implements a Geographically Weighted Regression (GWR) based SDB workflow as a Geographic Resources Analysis Support System (GRASS) GIS module (i.image.bathymetry). Several case studies were carried out to examine the performance of the module in multi-constellation and multi-resolution satellite imageries for different study areas. The results indicate a strong correlation between SDB and reference depth. For instance, case study 1 (Puerto Rico, Northeastern Caribbean Sea) has shown an coefficient of determination (R 2 ) of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R 2 of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R 2 of 0.93 and an RMSE of 1.65 m. The reference depths were acquired by using LiDAR for case study 1 and an echo-sounder for case studies 2 and 3. Further, the estimated SDB has been used as one of the inputs for the Australian National University and Geoscience Australia (ANUGA) tsunami simulation model. The tsunami simulation results also show close agreement with post-tsunami survey data. The i.mage.bathymetry module developed as a part of this study is made available as an extension for the Open Source GRASS GIS to facilitate wide use and future improvements.

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“…These efforts can be roughly classified into three methodological categories: (a) experimental [5][6][7]; (b) numerical/computational [8][9][10][11][12]; and (c) theoretical [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Various state-of-the-art discretization techniques can be and are used for this purpose, such as finite difference, finite element and finite volume models (see, e.g., [8,9,12,22,27]). Among these models, discontinuous Galerkin models (as described in, e.g., [28,29]) have recently become popular, because they combine numerical conservation properties with geometric flexibility, high-order accuracy and robustness on structured and unstructured grids.…”

Section: Introductionmentioning

confidence: 99%

An Experimental and Numerical Study of Long Wave Run-Up on a Plane Beach

Drähne

Goseberg

Vater

et al. 2015

JMSE

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This research is to facilitate the current understanding of long wave dynamics at coasts and during on-land propagation; experimental and numerical approaches are compared against existing analytical expressions for the long wave run-up. Leading depression sinusoidal waves are chosen to model these dynamics. The experimental study was conducted using a new pump-driven wave generator and the numerical experiments were carried out with a one-dimensional discontinuous Galerkin non-linear shallow water model. The numerical model is able to accurately reproduce the run-up elevation and velocities predicted by the theoretical expressions. Depending on the surf similarity of the generated waves and due to imperfections of the experimental wave generation, riding waves are observed in the experimental results. These artifacts can also be confirmed in the numerical study when the data from the physical experiments is assimilated. Qualitatively, scale effects associated with the experimental setting are discussed. Finally, shoreline velocities, run-up and run-down are determined and shown to largely agree with analytical predictions.

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Operational tsunami modelling with TsunAWI – recent developments and applications

Cited by 35 publications

References 30 publications

A Multiple-Parameter Methodology for Placement of Tsunami Sensor Networks

A Multiple-Parameter Methodology for Placement of Tsunami Sensor Networks

Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation

An Experimental and Numerical Study of Long Wave Run-Up on a Plane Beach

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