modelling of the 2005 flood event in carlisle
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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)
2
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flow )(
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Representation of flow between cells in LISFLOOD-FP
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Qflowflow
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
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RP
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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
45
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89
10
Sheepmount
Model
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rical
2.0 2.2 2.4 2.6 2.8 3.02.
02.
22.
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2
Cummersdale
Model
Empi
rical
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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
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Linstock
Model
Empi
rical
3.0 3.5 4.0 4.5 5.0
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Great Corby
Model
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rical
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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.
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