Probabilistic tsunami hazard assessment: quantifying uncertainty in landslide generated waves

Landslide generated waves (LGWs) have many associated uncertainties that need to be ac- counted for during a hazard analysis. The work presented in this thesis developed and applied numerical modelling techniques to investigate and quantify these sources of uncertainty. Firstly, to model the LGW source as a deformable slide, a smoothed particle hydrodynamics (SPH) simulator was improved and adapted. The simulator was tested using lab scale bench- marks and an idealised full scale LGW scenario. The effects of landslide source parameters on the wave at increasing scales were then investigated. In order to make use of the findings regarding complex LGW source models, a probabilistic sensitivity analysis on the full range of source parameters and their effect on the generated wave was performed using the SPH simulator. This showed that the geometric landslide parameters (such as volume and submergence depth) contributed more to uncertainty in the resulting wave characteristics near the source than the rheological parameters. By coupling different wave propagation models to the results from the near-field SPH simulator, it was revealed that the choice of mathematical formulation for propagation made a significant difference to which parameters affected the inundation level the most. These findings have important implications for the design of future LGW modelling studies and which parts of the model workflow should have more computational cost dedicated to them. Near the source the landslide geometry outweighs the complexity of the rheological model in terms of influence on the wave characteristics. During propagation the mathematical formulation chosen can have a large influence on results, so dedicating extra computational cost to this phase would be worthwhile.Open Acces

Uncertainty quantification of landslide generated waves using gaussian process emulation and variance-based sensitivity analysis

Simulations of landslide generated waves (LGWs) are prone to high levels of uncertainty. Here we present a probabilistic sensitivity analysis of an LGW model. The LGW model was realised through a smooth particle hydrodynamics (SPH) simulator, which is capable of modelling fluids with complex rheologies and includes flexible boundary conditions. This LGW model has parameters defining the landslide, including its rheology, that contribute to uncertainty in the simulated wave characteristics. Given the computational expense of this simulator, we made use of the extensive uncertainty quantification functionality of the Dakota toolkit to train a Gaussian process emulator (GPE) using a dataset derived from SPH simulations. Using the emulator we conducted a variance-based decomposition to quantify how much each input parameter to the SPH simulation contributed to the uncertainty in the simulated wave characteristics. Our results indicate that the landslide’s volume and initial submergence depth contribute the most to uncertainty in the wave characteristics, while the landslide rheological parameters have a much smaller influence. When estimated run-up is used as the indicator for LGW hazard, the slope angle of the shore being inundated is shown to be an additional influential parameter. This study facilitates probabilistic hazard analysis of LGWs, because it reveals which source characteristics contribute most to uncertainty in terms of how hazardous a wave will be, thereby allowing computational resources to be focused on better understanding that uncertainty

VarPy: A Python library for volcanology and rock physics data analysis

VarPy is an open-source toolbox which provides a Python framework for analysing volcanology and rock physics data. It provides several functions, which allow users to define their own workflows to develop models, analyses and visualisations. The goal of the VarPy library is to accelerate the uptake of computational methods by researchers in volcanology and rock physics. It does this via two mechanisms:- supplying a library of ready-made functions that are generally useful; and- providing a context for creating, sharing and comparing additional functions. We anticipate two groups of VarPy users:- the majority who use functions already written and in the library; they will predominantly arrange to use sequences of these functions with their own parameterisations; and- contributors, who, as well as using provided functions, also want to write additional functions for their own use or to add to the librar