visualising and communicating uncertain flood inundation maps
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Visualising and Communicating Uncertain Flood Inundation Maps. David Leedal 1 , Jeff Neal 2 , Keith Beven 1,3 and Paul Bates 2 . Lancaster Environment Centre, Lancaster University, Lancaster, UK; School of Geographical Sciences, University of Bristol, Bristol, UK; - PowerPoint PPT PresentationTRANSCRIPT
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 [email protected] to make sure you
have the most up to date URL