Jack Dignan

Jack Dignan

Profile Display Name:

Jack Dignan

E-mail Address:


Start Year

2020 (Cohort 7)

Research interests:

Tsunami Science, Coastal Inundation, High-Resolution Modelling, High-Performance Computing, Coastal Engineering

Hobbies and interests:

PhD Project
PhD Title

Enhancing overland flow tsunami modelling across urban topography with novel statistical emulation

Research Theme

Environmental Physics and Mathematical Modelling

Primary Supervisor
Primary Institution


Secondary Supervisor
Secondary Institution



Existing tsunami simulation models often rely on bare-earth topography to model inundation and overland flow, however, in reality, the flow of water inundating coastal regions is far more complex. The presence of buildings, structures, vegetation, etc. all influence the flow of water during tsunami inundation. However, tsunami models disregard terrestrial obstacles in favour of increased computation speeds. Statistical emulators can be used to produce approximations of complex computer models at much higher computation speeds than are achievable through simulation-based modelling. We look to design a custom tsunami simulator coupling an efficient transoceanic linear numerical scheme with a smoothed particle hydrodynamic (SPH) governed runup and inundation model capable of precision and stable modelling across complex topography, which will form the basis for our simulation runs later used to fit an emulator to our simulation results. This emulator will then be able to provide approximations of inundation extent and height accounting for the urban topography. It will crucially enable the propagation of uncertainties from tsunami sources to future possible impacts, a first at this scale. The proposed project will focus on the development of such an emulator given a particular case study (e.g. Japan, Indonesia, New Zealand, etc.), using GPU and KNL clusters. We will then test the developed methodology by applying it to another tsunami-prone region to identify transferability of the use of emulators for uncertain inundation mapping considering the urban topography. We will also test the accuracy and precision of this emulator by modelling past events and comparing the output produced by simulation models, emulators and reality using the formal framework of Bayesian calibration to tune our models.

Policy Impact
Background Reading
  • Jack Dignan, Aaron Micallef, Christof Mueller, Attilio Sulli, Elisabetta Zizzo, Daniele Spatola (2020) A scenario-based assessment of the tsunami hazard in Palermo, northern Sicily, and the southern Tyrrhenian Sea Subaqueous Mass Movements and their Consequences: Advances in Process Understanding, Monitoring and Hazard Assessments. Geological Society, London, Special Publications, 500 DOI: https://doi.org/10.1144/SP500-2019-181
  • Jack Dignan, Matthew Hayward, Dimitra Salmanidou, Mohammad Heidarzadeh, Serge Guillas (2023) Probabilistic landslide tsunami estimation in the Makassar Strait, Indonesia, using statistical emulation AGU Earth and Space Sciences DOI:

Conferences and Workshops
Training courses
  • Gaussian Process and Uncertainty Quantification Summer School, hosted by University of Sheffield. September 2021
  • Smoothed Particle Hydrodynamics (SPH) Short Course, hosted by University of Parma. October 2021
  • Fundamental Theory of Statistical Inference, hosted by London Taught Course Centre/Imperial College London. November 2021
  • Smoothed Particle Hydrodynamics Research and Engineering International Community (SPHERIC) Training Day, hosted by University of Catania/INGV Catania. June 2022

Social Links
University Departmental Website:

Personal Website:




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