Visualising and Communicating Uncertain Flood Inundation Maps

David Leedal1, Jeff Neal2, Keith Beven1,3 and Paul Bates2.

(1) Lancaster Environment Centre, Lancaster University, Lancaster, UK;(2) School of Geographical Sciences, University of Bristol, Bristol, UK;

(3) Geocentrum, Uppsala University, Uppsala, Sweden

Guidelines for flood risk mapping

Guidelines and framework for best practice in uncertain flood risk mapping (FRMRC2 WP1.7) provides:• A comprehensive background in state-of-the-art

thinking and methods for uncertainty analysis

• A breakdown of the flood risk modelling procedure into 7 key processes

• A series of decision trees for each process

• A set of case studies showing examples of the guidelines in action

Types of uncertainty

The Guidelines and Framework emphasises methods for aleatory and epistemic uncertainty.

• Aleatory: arising from the natural variability of the process

• Epistemic: shortcoming in knowledge about the process

Addressing epistemic uncertainty

Objective is to elicit and record expert opinion in a reflexive way and to document the thoughts, decisions and processes of those involved.

The ‘Guidelines and Framework’ suggests the modelling process should be documented in sufficient detail to provide a record of the decisions and methods used during the modelling exercise

What are the benefits of documenting a modelling exercise?

• Transparency – providing a record of which processes were carried out and why

• Which model was used and why?

• Which parameters were adjusted and within what range? Why?

• What topography was used?

• How where bridges treated?

• How many MC realisation were performed?

• etc…

What are the benefits of documenting a modelling exercise?

• Improve work practice – standardisation etc

• Method of communication with others

• Transfer skills and experience

• Receive support (and criticism)

These methods address epistemic uncertainty by:

• Explicitly communicating the degree to which a factor is understood

• Describing how a factor was addressed

• Making the process open so that others can:

• appreciate the degree of understanding

• contribute to better understanding if possible

• Over time produce a catalogue of cases that can be studied

In the mean time...

These methods address aleatory uncertainty:

• Monte Carlo

• Event generators

• GLUE

• Bayesian methods

• …many more (applied separately and in combination)

Carlisle uncertain flood inundation study

Carried out by Jeff Neal (Bristol) and Caroline Keef (JBA)

• Boundary condition upstream input event generator produced 47000 multivariate input scenarios (with model identified from observed level + rating curve record)

• LISFLOOD-FP 2D hydrodynamic model simulated flood spreading over 5m grid for each scenario (using HPC)

• 40GB data generated

• Frequency of depth exceedence for each model cell can be calculated from data set

Data visualisation

The ‘Guidelines and Framework’ outlines the need for a modelling study to provide a clear method to visualise the complex data sets produced by uncertainty analysis. This method should:

• Allow non-experts to gain an insight into the identified uncertainty in the study

• Provide a means to support decision making if necessary

The Google maps uncertain flood inundation visualisation tool

Things to look out for:

• Data stored centrally

• Familiar Google maps background and UI

• User friendly UI widgets

• Visual and text-based communication

• Wiki and bulletin board

The web-tool can be accessed from:

http://www.lancs.ac.uk/postgrad/leedald/Carlisle/visualisation.htmlThis address may change for future versions so please contact d.t.leedal@lancaster.ac.uk to make sure you

have the most up to date URL