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Assessing Uncertainty when Predicting Extreme Flood Processes.

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Presentation on theme: "Assessing Uncertainty when Predicting Extreme Flood Processes."— Presentation transcript:

1 Assessing Uncertainty when Predicting Extreme Flood Processes

2 Risk & Uncertainty (IMPACT WP5) Overview Aims & Objectives Defining Uncertainty Expressing Uncertainty Sources of Uncertainty Combining Uncertainty Conclusions Where are we now? The way forward

3 Uncertainty Aims & Objectives The typical problem to be solved is: Typically modelling undertaken for flood risk assessment, emergency planning etc. offers a prediction of likely conditions with no guidance upon the accuracy and reliability of the prediction. One example: The peak of a flood may be predicted to arrive after 3 hours, but is that prediction 3 hours plus / minus 10 minutes, or 3 hours plus / minus 1 hour? The problem to be solved is predicting the accuracy and uncertainty of individual modelling and combined modelling results. How do you deal with uncertainty?

4 Risk & Uncertainty Objectives Creation of an advisory group drawn from industry, to advise on R&D direction, and in particular outputs Assessment of model prediction uncertainty at start / mid / finish of project within each theme area –Theme leader responsibility Application of models to a combined case study (real or virtual) near completion of the project to demonstrate predictive abilities and uncertainty Guidelines / implications of modelling uncertainty in relation to application of modelling results

5 Uncertainty Defining Uncertainty “Uncertainty is a general concept that reflects our lack of sureness about something or someone, ranging from just short of complete sureness to an almost complete lack of conviction about an outcome” NRC (2000) ‘Risk analysis and Uncertainty in Flood Reduction Studies’. National Research Council (US). National Academic Press.

6 Uncertainty Expressing Uncertainty - examples Deliberate vagueness – ‘There is a high chance of breaching’ Ranking without quantifying – ‘Option A is safer than Option B’ Stating possible outcomes without stating likelihoods – ‘It is possible the embankment will breach’ Probabilities of events or outcomes – ‘There is a 10% chance of breaching’ Range of variables and parameters – ‘The design flow rate is 100 cumecs +/- 10%’ Confidence intervals – ‘There is a 95% chance that the design flow rate lies between 90 and 110 cumecs’ Probability distributions (see example)

7 Uncertainty An example of uncertainty affecting the end user... BCR for two flood defence options (say) BCR Option B > BCR Option A --> Option B is better? But Uncertainty information shows that Option B has a higher chance of achieving a BCR < 1 If the decision maker places a greater importance on BCR > 1, then Option A may become the preferred choice

8 Uncertainty Sources of Uncertainty

9 Uncertainty Combining Uncertainty Consider three approaches: General approach –Root mean square of uncertainty Simulation approach –Uncertainty expressed as probability distribution –Integrate through Monte Carlo techniques Sensitivity testing –variation in parameters –appropriate prior to more thorough techniques (1 & 2)

10 Uncertainty Sensitivity Testing Two approaches: HR BREACH model probability distribution for factor of safety in bank stability calculations –Distribution represents ‘all’ uncertainty Basic sensitivity assessment –Vary each parameter in turn –Assess sensitivity at different levels

11 Uncertainty Sensitivity Testing - HR BREACH

12 Risk & Uncertainty Sensitivity testing - HR Breach Example


14 Risk & Uncertainty Sensitivity Testing - Basic Parameters




18 Uncertainty Conclusions (1) Consideration of uncertainty provides the decision maker with additional information on which to base a decision. Consideration of uncertainty can therefore lead to different and more justifiable decisions than studies that do not include uncertainty.

19 Uncertainty Conclusions (2)  Uncertainty can stem from a variety of different sources. These sources can be generally categorised under two headings:  Natural Variability  Knowledge Uncertainty (These two categories are known by a variety of different names)

20 Uncertainty Conclusions (3)  Uncertainty can be presented or expressed and handled in a variety of different ways.  To facilitate incorporating uncertainty within the IMPACT project, specific (methodical) practices will be agreed (see paper for initial approach)

21 Uncertainty Where are we now? Where are we going? Reviewed concepts Identified three levels of approach Starting with simplest - sensitivity analyses Starting with breach; expanding to flood propagation and sediments Implement more detailed analysis as time and funding permits Seeking end user feedback on this approach

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