Development of a risk based procedure and Project ... · The Risk tools framework RFSM Surface Flooding Simulation Flood Depth – Damage Functions (HRW) InfoWorks CS Simulation Economic

Development of a risk based procedure and supporting tools for urban drainage

Dr Yannick CessesDr Yannick Cesses

IAHR UK Section – 16/09/2010

Dti Sam Project

Project Objective• Research to develop a risk based procedure for drainage

analysis and managementAiming to overcome some of the limitations of current methodsFollow the trend already set by River and Coastal defence

• Partners12 partners across the drainage industry

• Value£1,573,000 (DTI Funding 50%)

• Duration3+ years (March 2006 – June 2009)

SAM : System-based analysis and management of urban flood risks

SAM Project partners - research SAM Project partners – industry partners

Drainage engineering evolutionHealth

• Disease• Odour

Level of service • 30 year flooding protection• 100 year flooding protection

Environment awareness & protection • Sewerage separation• SUDS

Risk based approach• Consequences

Cost / Social / environment

The risk method for drainage analysis

< Integrated drainage >

Current tools

Wallingford Procedure (1981)(Wassp, Wallrus, HydroWorks, Infoworks)

• Focused on system performanceAnalysis of system performance based on design storms

– Matrix of Return Period and Duration events

Verification PlotRyde Catchment

Event D, Site 4016

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Time (minutes)

Disc

harg

e (m

/s)

Predict edObserved

Current limitations to drainage engineering

1. We know rainfall does not fall uniformly all over a large catchment and flooding arrives at different times – so how can we do properly integrated drainage assessment?

2. “People don’t care who’s flood water has caused the damage to their property”

SAM – research topic areas

Spatial time series rainfall (2D)• Stochastic rainfall generators and data

Newcastle University & Imperial College• Rainfall database and tools for spatial rainfall events• Assess the difference between using 2D rainfall and

uniform rainfall on urban drainage systemsDevelop a risk based procedure

• RFSM (rapid flood spreading tool)• SAM-UMC (integration of InfoWorks CS for multiple runs)• Risk shell for multiple runs and EAD analysis• Solutions development using risk based optimisation

Generation of “2D” rainfall

Imperial College & University of Newcastle • Stochastic models – “2D” time series rainfall• 2 approaches

Newcastle: data minimising approach, high and low resolution data (nested grids – spatially and temporally)Imperial: high resolution data (fixed grid)

• Calibrated to radar data – only 4 years of Nimrod data• Prototype useable tools

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Imperial College Newcastle

Rainfall data Management

Rainfall Files

CellID Time depth

11111

1st Jan 2003 00:001st Jan 2003 00:051st Jan 2003 00:101st Jan 2003 00:151st Jan 2003 00:20

5690.3

Rainfall Database

Rainfall Processing Tool

Amount of Data > 200G

Event analysis, selection and export tool

Event analysis and selection• Review summary statistics to support selection

Monthly / seasonal / annual rainfall totals, no. of wet days, annual maximum rainfall depth

• Identify localised intense events Identify all events that meet user-defined criteria (e.g. threshold intensity) in any cell within the study area/catchment

• Identify events over larger areasIdentify all events that meet user-defined criteria (e.g. threshold intensity) over a study area or entire catchment

Simulation and results

12345678

Result: scattering ellipses (2 correlated dependent variables)

FEH

FEH 95% upper bound

FEH – Uniform rainfall: 10% difference

Uniform – Spatial rainfall: 40% difference

Network flood volume analysisFlood volume deviation

Flood volume deviation in function of the size of the catchment

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1

1.2

50 100 500 1000 5000

Area in ha

Floo

d vo

lum

e de

viat

ion

*100

%

Flood volume analysis – 1D / 2D deviation

Rainfall deviation

System Loads – System States

20,000

35000

Extre

me

rain

fall

Time – 100 years

Dry

Freq

uent

rain

fall

Fully operational

Sys

tem

Sta

tes

–50

00 n

odes

Multiple system failure

Blockage + structural

Single asset failure

Blockage


Structural

Time

Dry

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uent

rainf

all

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me

rainf

all

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tem

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tes

Multiple system failure

Blockage + structural


Blockage


Structural

Fully operational

If Foul

If Foul

If Foul

System Loads – System States

Rapid Flood Spreading Model

RFSM – Developed specifically for probabilistic analysis• Simple overland flooding tool

– Rapid

• NAFRA (National Flood Risk Assessment)– Multiple loading events and defence system states, national-scale

• Modelling Decision Support Framework (MDSF)– Flood risk analysis software product being developed for use by the UK

Environment Agency staff, catchment scale

Development under DTI SAM• Application to the urban environment and below-ground

drainage systems

Rapid Flood Spreading Model

The Risk tools framework

RFSM SurfaceFlooding

Simulation

Flood Depth – Damage Functions

(HRW)

InfoWorks CSSimulation

Economic Damage

Calculation

RFSM ImpactZone Table

(pre-processed)

Sampled Rainfall events

National PropertyDataset or

Similar(GCC/HRW)

Risk Shell for convergence on

EAD

ProcessedGround Model

(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration

Probability of Failure Functions

of Assets

SAM – UMC Framework

Rainfall data

Analysis of Results

Model state Simulation Flood routing Damage calc’



Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results




Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results




Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results




Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results

Model state Simulation Flood routing Damage calc’Flood volumes



Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results

Model state Simulation Flood routing Damage calc’Flood volumes



Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results

Model state Simulation Flood routing Damage calc’Flood volumes Flood depths



Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results

Model state Simulation Flood routing Damage calc’Flood volumes Flood depths



Simulation


(HRW)


Economic Damage

Calculation


(pre-processed)



Similar(GCC/HRW)


EAD


(HRW)

LIDAR Data(GCC/SW)

InfoWorks CSModel

Configuration


of Assets


Rainfall data

Analysis of Results

Model state Simulation Flood routing Damage calc’Flood volumes Flood depths Flood damages

Risk based tools structure

SAM-UMC

InfoworksCS ModelsRainfall Events

SAM-UMC

EAD

Return Period

Define Return Period(Start at 1 or 2 years)

No flooding

EAD for network & Impact Zones

All critical durations and return periods run ?

No

Define a duration(Start at 30min)

EAD convergence at control nodes \ all nodes ?

= marginal EAD increase(1-5%)

Yes

Yes

Damage calculation No

Design rainfall - EAD

30 min2 hrs

5 hrs8 hrs

Top of system

Bottom end

Evaluate extreme event threshold

Process rainfall series

Run each event •Extreme events•Normal events

•Dry event

Damage calculation

EAD convergence at control nodes \ all nodes ? = marginal EAD deviation

(1-5%)

EAD for network & Impact Zones

No

Yes

Exte

nd r

ainf

all

seri

es

AVG Damage

Run ID

Davg

1 2 3 4 5 …

Time series - EAD

EAD categories

Sub-division of information for EAD

Weather

Dry weather Frequent event Extreme event

System state

Fully functional NO DAMAGE NO DAMAGE DAMAGE

Collapse(s) and/or

Blockage(s)

DAMAGE (IF FOUL PIPE)

DAMAGE DAMAGE

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50

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400

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Node ID

EA

D at

eac

h no

de (£

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StructuralBlockageHydraulic

EAD at each node

Progressive EAD

0.00E+00

1.00E+06

2.00E+06

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1.00E+07

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RP (years)

Dam

age

(£)

Attribution of EAD

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Calculation of (annualised) Risk

Expected Annual Damage (EAD) calculated for each Impact Zone

• Integration of all possible events to find an annualised value of Likelihood x Consequence

Critical duration of each nodeis used for each return period

Damage

Probability

20P10P1P

Calculation of (annualised) Risk

Expected Annual Damage (EAD) calculated for each Impact Zone & manhole

• Integration of all possible events to find an annualised value of Likelihood x Consequence

Critical duration of each nodeis used for each return period

Damage

Probability

20P10P1P

Impact zone manhole

EAD – Impact zones and Assets

EAD – a function of pipe length Flood frequency (level of service)

•Flooding frequency is still probably a fundamental measure

• An EAD value could be from massive damage from very rare events, or damage from relatively frequent events.

Optimisation

EAD is a measure of performance of the existing system and does not tell us how to manage or to improve it.

Solutions development EAD for IZs and Assets does not solve any flooding problems, it just provides a measure of performance (current or future).

• Option 1• Traditional technique – use engineering judgement

Base decisions on reducing (or zero) flooding at selected Impact ZonesThen re-evaluate EAD, assess cost-benefit

• Option 2• Optimisation – use GA technique for evaluating

specific objective functionMaximise EAD reduction for given investmentMinimise investment for specified EAD reduction (network, nodes or Impact zones).

Solutions – Risk based Optimisation

Advantages• Efficient search for possible

solutions• Freedom to consider a range of

possible changes to networkDisadvantages

• Need to limit number of options to make run-times manageable

• requires pre-processing of the model

• Careful selection of search criteria

Solutions development

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500,000.00

1,000,000.00

1,500,000.00

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Generation

Cap

ital c

ost (

£)

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400,000.00

600,000.00

800,000.00

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1,200,000.00

EAD

(£)

Conclusion

• DTI SAM – is a radical new approach to analysis of system performance & asset management.

• Extendable to all aspects of drainage (environmental impact etc.)

subject to ability to use appropriate cost functions as a measure of impact)

Where do we go from here?

Discussion on the appropriateness of using EAD

• Is the same value of EAD appropriate for foul and surface water flooding?

• Is an area with the same value of EAD as another, that suffers from frequent rainfall compared to rare events, of more or less importance to provide a solution?

Weighting is effectively provided to frequent events• Is EAD fair? (Equity is a primary measure of

Sustainability)

EAD discussion

EAD on flooding could extend to cover:• Property damage, • Infrastructure damage,• Flood incidence costs,• Social trauma and health,• Mortality• National productivity impact

And then also:• Environmental impact

Pollution, Biodiversity, Carbon??

• Is this a way of measuring Sustainability or Resilience??• Perhaps “£” is not a universally appropriate indicator

Prognosis for the future

• DTI SAM – is a radical new approach to analysis of system performance & asset management in line with the new SRM.

• Future take-up of the method requires:Support from Policy makers and Regulators (OFWAT, Environment Agency, Defra)

Documents

Development of a risk based procedure and Project ... · The Risk tools framework RFSM Surface Flooding Simulation Flood Depth – Damage Functions (HRW) InfoWorks CS Simulation Economic