Work Package 2From data theoretic to knowledge theoretic approaches to flood

modelling

Dr Nick Odoni and Professor Stuart LaneUniversity of Durham

Work Package 2 aims• To develop a Minimum Information Requirement model for flood risk

– Focus on diffuse land management activities– Primarily using secondary data sets and new data where necessary

• To evaluate model performance– sensitivity and propagation of uncertainty– formal model assessment

• To simulate the effects of diffuse land management impacts on flood risk

• To undertake this work within the framework of Environmental Competency Groups (see Work Package 3)

PhilosophyOdoni and Lane, in press, Progress in Physical

Geography

Experience

Data Theory

Model-Theoretic Model-Data

Making models‘But then you don’t even have to have

reservoirs as such. You could have a dam with a restricted outflow so it literally holds it back and releases it slowly…so the stream normally just runs straight through it and doesn’t get stopped…’ [Local member]

‘And the other problem is that once you get down to Newtondale, you have got the railway there, which means you are causing problems at the railway, they are not going to be pleased about that.’ [Local member]

‘So to protect Pickering, the nearer the dams are to Pickering the better?’ [Local member 1] ‘Well yes certainly’ [Local member 2]

‘the man has taken the flood banks down... and he’s put a bridge across the river at that point, and the bridge is an old articulated lorry body, which enables him to go backwards and forwards with his farm implements when he’s making silage, then the information that they would be able to collect from Ings Bridge would be useless…Because it wouldn’t be going through the gauge, it would be going over land.’ [Local member]

• Perceptualisation, conceptualisation, framing, of the problem

• What to try where• Why things won’t work• Why things (e.g. data)

are wrong

The models• Bunding

– Pickering• Assumes conditions of high

Standard Percentage Runoff• Identifies a critical flood onset

– which becomes the target to which flows must be reduced

• Determines flow directions across the landscape using digital topographic data

• Applies a rainfall rate to those flow directions

• Routes the associated rainfall • Produces a flow discharge rate

at every point in the catchment

• Provides a simple interactive tool to place multiple holy bunds

– enter their characteristics– assess the duration that

they can store water for – in relation to preventing

flows at Pickering reaching the critical flood onset

• Validation by comparison with ISIS-TUFLOW

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Time (minutes) from 00:00 25th June 2007

Gauged rain rate (mm/15 min)

Radar rain rate (mm/15 min)

The models

• Demonstration available, for Uckfield, during Conference

OverflowUckfield and Pickering

•Assumes conditions of high Standard Percentage Runoff•Identifies a critical flood onset

- which becomes the target to which flows must be reduced

•Determines flow directions across the landscape using digital topographic data•Applies different rainfall rates to those flow directions•Routes the associated rainfall for each rainfall rate

•Allows depth dependent routing rates•Samples from routing rates according to event rainfall•Integrated into time-dependent travel time maps, which are convolved with time-dependent rainfall rates, to provide time-varying flow discharge at every point in the catchment

•Development for uncertainty and scenario analysis in Pickering

•InterventionsRemeandering

Riparian vegetationDebris damsChannel reprofilingHedgerowsWoodland beltsBunds

Bunding : a demonstration

• Files we prepared earlier– Pit filling– Flow routing (FD8 on

hillslopes, D8 in channels)

– Cut the catchment– Order the channel

networks

• Our demonstration– Stage I

• Place the dams• Calculate potential

storage according to bund height

– Stage II• Test impacts on flood flow

to identify how long Pickering (in this example) can be protected for at a given rain rate

Hybrid models

• Models as a form of ‘covering law’ – the model can ‘travel’

• ‘Hybrid’ models: – the loss of the ‘generic’

rationale (but aren’t all models hybrid?)

– the loss of technological lock-in: the models are forced to mutate

Verification: Have each of these

stages been done correctly? Do not attempt validation until

verification is complete

Yes

Use model to simulate or predict future events or

other areas No

Conceptualise model

Identify relevant rules from conceptualisation

Validation: compare predictions with

observations

Calibration: optimise model predictions by changing parameter

values

Express rules mathematically

Solve rules using a simulation model

Apply model to a set of boundary conditions and using expected parameter

values

Decision: are predictions good enough? Is the model acceptable?

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2

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3

If calibration is necessary, then the model should be re-validated using a different

validation data set to that used for calibration.

The ‘peculiar’ model

The specific case

The calibrated model

The generic model

Participating Institutions

Funding Body

http://knowledge-controversies.ouce.ox.ac.uk/