Modelling of the 2005 flood event in Carlisle

Jeff Neal1, Paul Bates1,

Tim Fewtrell, Matt Horritt, Nigel Wright, Ignacio Villanuaver, Sylvia Tunstall, Hazel Faulkner, Tom Coulthard, Jorge Ramirez, Caroline

Keef2, Keith Beven and David Leedal3

1School of Geographical Sciences, University Road, University of Bristol, Bristol. BS8 1SS.2JBA Consulting, South Barn, Broughton Hall, Skipton, N Yorkshire, BD23 3AE, UK.

3Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK.

• Carlisle 2005 event data• Overview• Issues

• Inundation modelling• Channel hydraulics and gauges• Structural complexity• Resolution• New numerical scheme

• Beyond inundation modelling• Urban futures• Probabilistic flood risk at confluences

Introduction

2005 event data

2005 event data

2005 event data

Channel hydraulics and gauges

1D/2D model complexity

1D/2D model complexity

Urban floodplain processes

Neal et al., 2009

25 m resolution10 m resolution5 m resolution

A new LISFLOOD-FP formulation

• Continuity Equation• Continuity equation relating flow fluxes and change in cell depth

• Momentum Equation• Flow between two cells is

calculated using:

• Manning’s equation (ATS)

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Representation of flow between cells in LISFLOOD-FP

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flow

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A new LISFLOOD-FP formulation

A new LISFLOOD-FP formulation

Probabilistic flood risk mapping at confluences

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RP

Q

RP

Q

RP

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RP

The problem at confluences

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Set Δ of m gauges. Each is a random variable X at location i

Marginal distributions at each location Yi

Conditional distribution, spatial dependence

Simulate events over time t (e.g. 100 years) when y at Yi is greater than u

Sample from data at gauges Δ(Block bootstrapping)

The problem at confluences

• Model the conditional distribution of a set of variables given that one of these variables exceeds a high threshold.

Event simulation with spatial dependence

Set Δ of m gauges. Each is a random variable X at location i

Marginal distributions at each location Yi

Conditional distribution (spatial dependence)

Simulate events over time t (e.g. 100 years) when y at Yi is greater than u

Sample from data at gauges Δ(Block bootstrapping)

The problem at confluences (uncertainty)

• Model the conditional distribution of a set of variables given that one of these variables exceeds a high threshold.

Refit to data and run event generator may times to approximate uncertainty

Hydraulic modelling

4.0 4.5 5.0 5.5 6.0 6.5 7.0

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Sheepmount

Model

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rical

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02.

22.

42.

62.

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2

Cummersdale

Model

Empi

rical

1.0 1.2 1.4 1.6 1.8

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1.2

1.4

1.6

1.8

2.0

2.2

Harraby Green

Model

Empi

rical

3.4 3.6 3.8 4.0 4.2 4.4

3.5

4.0

4.5

5.0

5.5

Linstock

Model

Empi

rical

3.0 3.5 4.0 4.5 5.0

3.0

3.5

4.0

4.5

5.0

5.5

6.0

Great Corby

Model

Empi

rical

2.2 2.4 2.6 2.8 3.0 3.2 3.4

2.5

3.0

3.5

Greenholme

Model

Empi

rical

Hydraulic modelling

• LISFLOOD-FP hydraulic model (Bates et al., 2010)• 1D diffusive channel model• 2D floodplain model at 10 m resolution• Model calibrated on 2005 flood event (RMSE 0.25 m).

Hydraulic modelling

• LISFLOOD-FP hydraulic model (Bates et al., 2010)• 1D diffusive channel model• 2D floodplain model at 10 m resolution• Model calibrated on 2005 flood event (RMSE 0.25 m).

• Event simulation• 47000 events• Scaled 2005 hydrographs• Event simulation time was 0.1-2 hours• Analysis took 5 days and generated 40 GB of data

Run 1 flood frequency• Run 1 of the event generator using all flow data

Run 1 flood frequency

• The maximum flood outline was a combination of multiple events.

• Cannot assume same return period on all tributaries

Uncertainty in the 1 in 100 yr flood outline

Risk• MasterMap building outlines• Depth damage curve• Calculate damage from each event

Conclusions• Flooding at confluences is critical to the basin-wide development of

flood hazard and depends on the joint spatial distribution of flows.• Assuming steady state flows over predicted flood hazard for a range of

flows and event durations.• The maximum flood outline was a combination of multiple events.

• Cannot assume the same return period on all tributaries• Risk assessment using the event data was demonstrated.

• Expected damages increase nonlinearly. • As expected a few events caused most of the damage.