eprintid: 205
rev_number: 4
eprint_status: archive
userid: 6
dir: disk0/00/00/02/05
datestamp: 2009-01-27
lastmod: 2015-05-29 19:49:22
status_changed: 2009-04-08 16:55:55
type: report
metadata_visibility: show
item_issues_count: 0
creators_name: Hocking, Graeme
creators_name: Jakeman, John
creators_name: Sexton, Jane
creators_name: Wand, Matt
title: Tsunami risk modelling for Australia: understanding the impact of data
ispublished: pub
subjects: other
subjects: telecom
studygroups: misg25
companyname: Geoscience Australia
full_text_status: public
abstract: Modelling the impacts from tsunami events is a complex task. A simplification is obtained by taking a hybrid approach where two different models are combined: relatively simple and fast models are used for simulating the tsunami event and the wave propagation through open water. The impact from tsunami inundation is simulated with another type of model which is suitable for resolving the details of the run-up process and the resulting inundation. The inundation modelling is conducted using the ANUGA model which is a result of collaboration between the Australian National University and Geoscience Australia. It solves the 2D nonlinear shallow water wave equations using a finite volume method.

One of the critical requirements for reliable inundation modelling is an accurate model of the earth's surface that extends from the open ocean through the inter-tidal zone into the onshore areas to be studied. Production of a sufficiently accurate elevation model is a complex and difficult process made more difficult because the available elevation data inevitably will come from a number of different sources and will have a range of vintages, resolutions and reliability.

There are two questions that arise when data is requested. The first deals with the true variability of the topography. Obviously, a flat surface needn’t be sampled nearly as finely as a highly convoluted surface. The second question relates to sensitivity; how are error bars derived for the impact results if the error bars on each elevation point is known? ANUGA solves the 2D nonlinear shallow water wave equations using a finite volume method and typical models can take days of computational time, so proper sensitivity analyses are often prohibitively expensive in terms of computational resources.

The main aim of this project was therefore to understand the uncertainties in the outputs of the inundation model based on possible uncertainty in the input data.
problem_statement: Geoscience Australia’s desired outcomes from the study group are:

1. Develop an understanding of the sensitivities of the inundation model to the horizontal resolution of elevation data. In particular,
a. What is the relationship between the input wave in terms of amplitude, period, momentum and depth; and “wave number” or “variability” of the elevation data through which the wave is propagating?
b. What sampling guidelines apply to the elevation data given a particular input wave and scale of the study area?
c. Given a known elevation data set and a particular scenario, what can be said about the reliability of the model results? In other words: what is the required spatial resolution to model a tsunami wave “well enough”?

2. Develop an understanding of the sensitivities of the inundation model to the vertical uncertainty in the elevation data. In particular,
a. With a given input wave and data set, what errors exist in the modelled wave?
b. What is the relationship between the input wave (in terms of period or energy) and vertical accuracy of the input data?
c. What is the required vertical accuracy in the elevation data to model a tsunami wave “well enough”?
d. What can be said about how high-precision but low-accuracy data could affect or bias the results?
e. How is the accuracy of the modelled wave dependant on the complexity of the elevation data? That is, what degree of amplification and/or focusing occurs as a result of the accuracy of the elevation data?

where the inundation model is based on the 2D nonlinear shallow water wave equations.

Once the uncertainties in the data are characterised, and the sensitivity of the model is known (estimated), the next step might be to develop wave inundation/amplification classes whereby similar geometries can by treated as a class where factors such as roughness, slope, degree of focussing, etc are captured.
The final outcome would then be to

3. Develop an understanding of how the following two uncertainties affect the modelled inundation results:
a. that which is associated with the uncertainty in the accuracy of the data at any one location, and
b. that which is associated with the generalisation of a set of similar sites to an inundation class.
date: 2008
date_type: published
pages: 14
citation:   Hocking, Graeme and Jakeman, John and Sexton, Jane and Wand, Matt  (2008) Tsunami risk modelling for Australia: understanding the impact of data.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/205/1/misg2008geoscience.pdf