loading page

Probabilistic Quantification of Tsunami Currents in Karachi Port, Makran Subduction Zone, using Statistical Emulation
  • Devaraj Gopinathan,
  • Mohammad Heidarzadeh,
  • Serge Guillas
Devaraj Gopinathan
University College London, University College London

Corresponding Author:[email protected]

Author Profile
Mohammad Heidarzadeh
Brunel University London, Brunel University London
Author Profile
Serge Guillas
University College London, University College London
Author Profile

Abstract

In this paper, we model the full range of possible local impacts of future tsunamis in the Makran subduction zone (MSZ) at Karachi port, Pakistan. For the first time, the 3-D subduction geometry Slab2 is employed in the MSZ, in conjunction with the most refined rupture segmentation to date for this region, to improve the earthquake source definition. Motivated by the massive sediment layer over the MSZ, we also introduce to tsunami modeling the application of the sediment amplification formula, resulting in enhancements of seabed deformation up to 60% locally. Furthermore, we design a new unstructured mesh algorithm for our GPU-accelerated tsunami code in order to efficiently represent flow velocities, including vortices, down to a resolution of 10m in the vicinity of the port. To afford to compute very large number of high resolution tsunami scenarios, for the granularity and extent of the range of magnitudes (occurrence ratios of 1:100,000 implied by the Gutenberg-Richter relation) and locations of source, we create a statistical surrogate (i.e. emulator) of the tsunami model. Our main contribution is hence the largest set of emulated predictions using any realistic tsunami code to date: 1 million per location. We go on to obtain probabilistic representations of maximum tsunami velocities and heights at around 200 locations in the port area of Karachi. Amongst other findings, we discover substantial local variations of currents and heights. Hence we argue that an end-to-end synthesis of advanced physical, numerical and statistical modeling is instrumental in coastal engineering to comprehensively model local impacts of tsunamis.