Tsunami propagation modelling – a sensitivity study

Abstract. Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991) utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland). The sensitive model parameters (of 11 total considered) include wind drag, the depth-induced breaking γ B , and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters, and depth-induced breaking α B ) and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large number of interactions between parameters and a nonlinear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.

Section: Flow Bottom Frictionmentioning

confidence: 99%

Parameter sensitivity and uncertainty analysis for a storm surge and wave model

Bastidas¹,

Knighton

Kline³

2016

“…In tsunami models the bottom friction components f x and f y are usually expressed in terms of the fluxes M, N and of a friction coefficient C f , which in turn can be given in terms of the Manning's roughness coefficient n (see Satake and Tanioka, 1997;Dao and Tkalich, 2007), the final result being…”

Section: Background Theorymentioning

confidence: 99%

The UBO-TSUFD tsunami inundation model: validation and application to a tsunami case study focused on the city of Catania, Italy

Tinti¹,

Tonini

2013

Abstract. Nowadays numerical models are a powerful tool in tsunami research since they can be used (i) to reconstruct modern and historical events, (ii) to cast new light on tsunami sources by inverting tsunami data and observations, (iii) to build scenarios in the frame of tsunami mitigation plans, and (iv) to produce forecasts of tsunami impact and inundation in systems of early warning. In parallel with the general recognition of the importance of numerical tsunami simulations, the demand has grown for reliable tsunami codes, validated through tests agreed upon by the tsunami community. This paper presents the tsunami code UBO-TSUFD that has been developed at the University of Bologna, Italy, and that solves the non-linear shallow water (NSW) equations in a Cartesian frame, with inclusion of bottom friction and exclusion of the Coriolis force, by means of a leapfrog (LF) finite-difference scheme on a staggered grid and that accounts for moving boundaries to compute sea inundation and withdrawal at the coast. Results of UBO-TSUFD applied to four classical benchmark problems are shown: two benchmarks are based on analytical solutions, one on a plane wave propagating on a flat channel with a constant slope beach; and one on a laboratory experiment. The code is proven to perform very satisfactorily since it reproduces quite well the benchmark theoretical and experimental data. Further, the code is applied to a realistic tsunami case: a scenario of a tsunami threatening the coasts of eastern Sicily, Italy, is defined and discussed based on the historical tsunami of 11 January 1693, i.e. one of the most severe events in the Italian history.

“…The models have also been implemented widely by different researchers to simulate tsunami propagations and wave heights in Pacific, Atlantic, and Indian Oceans, with zoom in at particular areas of Caribbean, Japanese, Russian, South China, Mediterrenean seas and Gulf of Cadiz (Yalciner et al, 2000(Yalciner et al, , 2001(Yalciner et al, , 2002Baptista et al, 2003;Zahibo et al,2003;Yalciner, 2004;Annunziato, 2007;Dao and Tkalich, 2007;Yolsal et al, 2007;Franchello ,2008;Franchello and Krausmann, 2008;Kaabouben et al, 2008;Dao, et al, 2009;Franchello, 2010;Yolsal and Taymaz, 2010;Cruz et al, 2011;Ulutas, 2011). Although the SWAN (Mader, 1988) model was adapted to the TEWS, which is a part of GDACS, all included models might be used manually in order to predict tsunami arrival times and maximum heights.…”

Section: Post-event Analysismentioning

confidence: 99%

Web-based Tsunami Early Warning System: a case study of the 2010 Kepulaunan Mentawai Earthquake and Tsunami

Ulutaş

Inan

Annunziato³

2012

<p><strong>Abstract.</strong> This study analyzes the response of the Global Disasters Alerts and Coordination System (GDACS) in relation to a case study: the Kepulaunan Mentawai earthquake and related tsunami, which occurred on 25 October 2010. The GDACS, developed by the European Commission Joint Research Center, combines existing web-based disaster information management systems with the aim to alert the international community in case of major disasters. The tsunami simulation system is an integral part of the GDACS. In more detail, the study aims to assess the tsunami hazard on the Mentawai and Sumatra coasts: the tsunami heights and arrival times have been estimated employing three propagation models based on the long wave theory. The analysis was performed in three stages: (1) pre-calculated simulations by using the tsunami scenario database for that region, used by the GDACS system to estimate the alert level; (2) near-real-time simulated tsunami forecasts, automatically performed by the GDACS system whenever a new earthquake is detected by the seismological data providers; and (3) post-event tsunami calculations using GCMT (Global Centroid Moment Tensor) fault mechanism solutions proposed by US Geological Survey (USGS) for this event. The GDACS system estimates the alert level based on the first type of calculations and on that basis sends alert messages to its users; the second type of calculations is available within 30–40 min after the notification of the event but does not change the estimated alert level. The third type of calculations is performed to improve the initial estimations and to have a better understanding of the extent of the possible damage. The automatic alert level for the earthquake was given between Green and Orange Alert, which, in the logic of GDACS, means no need or moderate need of international humanitarian assistance; however, the earthquake generated 3 to 9 m tsunami run-up along southwestern coasts of the Pagai Islands where 431 people died. The post-event calculations indicated medium-high humanitarian impacts.</p>